ImpulseDE: detection of differentially expressed genes in time series data using impulse models.

PubMed

Sander, Jil; Schultze, Joachim L; Yosef, Nir

2017-03-01

Perturbations in the environment lead to distinctive gene expression changes within a cell. Observed over time, those variations can be characterized by single impulse-like progression patterns. ImpulseDE is an R package suited to capture these patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes across time points from a single or between two time courses from two experiments. To optimize running time, the code uses clustering and multi-threading. By applying ImpulseDE , we demonstrate its power to represent underlying biology of gene expression in microarray and RNA-Seq data. ImpulseDE is available on Bioconductor ( https://bioconductor.org/packages/ImpulseDE/ ). niryosef@berkeley.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data.

PubMed

Wang, Zhuo; Jin, Shuilin; Liu, Guiyou; Zhang, Xiurui; Wang, Nan; Wu, Deliang; Hu, Yang; Zhang, Chiping; Jiang, Qinghua; Xu, Li; Wang, Yadong

2017-05-23

The development of single-cell RNA sequencing has enabled profound discoveries in biology, ranging from the dissection of the composition of complex tissues to the identification of novel cell types and dynamics in some specialized cellular environments. However, the large-scale generation of single-cell RNA-seq (scRNA-seq) data collected at multiple time points remains a challenge to effective measurement gene expression patterns in transcriptome analysis. We present an algorithm based on the Dynamic Time Warping score (DTWscore) combined with time-series data, that enables the detection of gene expression changes across scRNA-seq samples and recovery of potential cell types from complex mixtures of multiple cell types. The DTWscore successfully classify cells of different types with the most highly variable genes from time-series scRNA-seq data. The study was confined to methods that are implemented and available within the R framework. Sample datasets and R packages are available at https://github.com/xiaoxiaoxier/DTWscore .

GEsture: an online hand-drawing tool for gene expression pattern search.

PubMed

Wang, Chunyan; Xu, Yiqing; Wang, Xuelin; Zhang, Li; Wei, Suyun; Ye, Qiaolin; Zhu, Youxiang; Yin, Hengfu; Nainwal, Manoj; Tanon-Reyes, Luis; Cheng, Feng; Yin, Tongming; Ye, Ning

2018-01-01

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a 'desirable' or 'user-defined' pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users' requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.

ReTrOS: a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression data.

PubMed

Minas, Giorgos; Momiji, Hiroshi; Jenkins, Dafyd J; Costa, Maria J; Rand, David A; Finkenstädt, Bärbel

2017-06-26

Given the development of high-throughput experimental techniques, an increasing number of whole genome transcription profiling time series data sets, with good temporal resolution, are becoming available to researchers. The ReTrOS toolbox (Reconstructing Transcription Open Software) provides MATLAB-based implementations of two related methods, namely ReTrOS-Smooth and ReTrOS-Switch, for reconstructing the temporal transcriptional activity profile of a gene from given mRNA expression time series or protein reporter time series. The methods are based on fitting a differential equation model incorporating the processes of transcription, translation and degradation. The toolbox provides a framework for model fitting along with statistical analyses of the model with a graphical interface and model visualisation. We highlight several applications of the toolbox, including the reconstruction of the temporal cascade of transcriptional activity inferred from mRNA expression data and protein reporter data in the core circadian clock in Arabidopsis thaliana, and how such reconstructed transcription profiles can be used to study the effects of different cell lines and conditions. The ReTrOS toolbox allows users to analyse gene and/or protein expression time series where, with appropriate formulation of prior information about a minimum of kinetic parameters, in particular rates of degradation, users are able to infer timings of changes in transcriptional activity. Data from any organism and obtained from a range of technologies can be used as input due to the flexible and generic nature of the model and implementation. The output from this software provides a useful analysis of time series data and can be incorporated into further modelling approaches or in hypothesis generation.

Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm.

PubMed

Tchagang, Alain B; Phan, Sieu; Famili, Fazel; Shearer, Heather; Fobert, Pierre; Huang, Yi; Zou, Jitao; Huang, Daiqing; Cutler, Adrian; Liu, Ziying; Pan, Youlian

2012-04-04

Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space. We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (Plasmodium chabaudi), systemic acquired resistance in Arabidopsis thaliana, similarities and differences between inner and outer cotyledon in Brassica napus during seed development, and to Brassica napus whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples. Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.

A cluster merging method for time series microarray with production values.

PubMed

Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio

2014-09-01

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

Volatility of linear and nonlinear time series

NASA Astrophysics Data System (ADS)

Kalisky, Tomer; Ashkenazy, Yosef; Havlin, Shlomo

2005-07-01

Previous studies indicated that nonlinear properties of Gaussian distributed time series with long-range correlations, ui , can be detected and quantified by studying the correlations in the magnitude series ∣ui∣ , the “volatility.” However, the origin for this empirical observation still remains unclear and the exact relation between the correlations in ui and the correlations in ∣ui∣ is still unknown. Here we develop analytical relations between the scaling exponent of linear series ui and its magnitude series ∣ui∣ . Moreover, we find that nonlinear time series exhibit stronger (or the same) correlations in the magnitude time series compared with linear time series with the same two-point correlations. Based on these results we propose a simple model that generates multifractal time series by explicitly inserting long range correlations in the magnitude series; the nonlinear multifractal time series is generated by multiplying a long-range correlated time series (that represents the magnitude series) with uncorrelated time series [that represents the sign series sgn(ui) ]. We apply our techniques on daily deep ocean temperature records from the equatorial Pacific, the region of the El-Ninõ phenomenon, and find: (i) long-range correlations from several days to several years with 1/f power spectrum, (ii) significant nonlinear behavior as expressed by long-range correlations of the volatility series, and (iii) broad multifractal spectrum.

Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data.

PubMed

Kim, Jongrae; Bates, Declan G; Postlethwaite, Ian; Heslop-Harrison, Pat; Cho, Kwang-Hyun

2008-05-15

Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles. A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in Dictyostelium discoideum, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized Dictyostelium cells. The software used in this article is available from http://sbie.kaist.ac.kr/software

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

PubMed Central

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P.

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis. PMID:23586021

Time series expression analyses using RNA-seq: a statistical approach.

PubMed

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

Developing a comprehensive time series of GDP per capita for 210 countries from 1950 to 2015

PubMed Central

2012-01-01

Background Income has been extensively studied and utilized as a determinant of health. There are several sources of income expressed as gross domestic product (GDP) per capita, but there are no time series that are complete for the years between 1950 and 2015 for the 210 countries for which data exist. It is in the interest of population health research to establish a global time series that is complete from 1950 to 2015. Methods We collected GDP per capita estimates expressed in either constant US dollar terms or international dollar terms (corrected for purchasing power parity) from seven sources. We applied several stages of models, including ordinary least-squares regressions and mixed effects models, to complete each of the seven source series from 1950 to 2015. The three US dollar and four international dollar series were each averaged to produce two new GDP per capita series. Results and discussion Nine complete series from 1950 to 2015 for 210 countries are available for use. These series can serve various analytical purposes and can illustrate myriad economic trends and features. The derivation of the two new series allows for researchers to avoid any series-specific biases that may exist. The modeling approach used is flexible and will allow for yearly updating as new estimates are produced by the source series. Conclusion GDP per capita is a necessary tool in population health research, and our development and implementation of a new method has allowed for the most comprehensive known time series to date. PMID:22846561

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

PubMed

Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

2015-01-01

Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes

PubMed Central

Manning, Cerys; Rattray, Magnus

2017-01-01

Multiple biological processes are driven by oscillatory gene expression at different time scales. Pulsatile dynamics are thought to be widespread, and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in expression dynamics, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. This underlies a new challenge to the experimentalist because neither intuition nor pre-existing methods work well for identifying oscillatory activity in noisy biological time series. Thus, there is an acute need for an objective statistical method for classifying whether an experimentally derived noisy time series is periodic. Here, we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of non-parametric regression with Gaussian processes. Our method can distinguish oscillatory gene expression from random fluctuations of non-oscillatory expression in single-cell time series, despite peak-to-peak variability in period and amplitude of single-cell oscillations. We show that our method outperforms the Lomb-Scargle periodogram in successfully classifying cells as oscillatory or non-oscillatory in data simulated from a simple genetic oscillator model and in experimental data. Analysis of bioluminescent live-cell imaging shows a significantly greater number of oscillatory cells when luciferase is driven by a Hes1 promoter (10/19), which has previously been reported to oscillate, than the constitutive MoMuLV 5’ LTR (MMLV) promoter (0/25). The method can be applied to data from any gene network to both quantify the proportion of oscillating cells within a population and to measure the period and quality of oscillations. It is publicly available as a MATLAB package. PMID:28493880

Construction of regulatory networks using expression time-series data of a genotyped population.

PubMed

Yeung, Ka Yee; Dombek, Kenneth M; Lo, Kenneth; Mittler, John E; Zhu, Jun; Schadt, Eric E; Bumgarner, Roger E; Raftery, Adrian E

2011-11-29

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

Quantitative assessment of effect of preanalytic cold ischemic time on protein expression in breast cancer tissues.

PubMed

Neumeister, Veronique M; Anagnostou, Valsamo; Siddiqui, Summar; England, Allison Michal; Zarrella, Elizabeth R; Vassilakopoulou, Maria; Parisi, Fabio; Kluger, Yuval; Hicks, David G; Rimm, David L

2012-12-05

Companion diagnostic tests can depend on accurate measurement of protein expression in tissues. Preanalytic variables, especially cold ischemic time (time from tissue removal to fixation in formalin) can affect the measurement and may cause false-negative results. We examined 23 proteins, including four commonly used breast cancer biomarker proteins, to quantify their sensitivity to cold ischemia in breast cancer tissues. A series of 93 breast cancer specimens with known time-to-fixation represented in a tissue microarray and a second series of 25 matched pairs of core needle biopsies and breast cancer resections were used to evaluate changes in antigenicity as a function of cold ischemic time. Estrogen receptor (ER), progesterone receptor (PgR), HER2 or Ki67, and 19 other antigens were tested. Each antigen was measured using the AQUA method of quantitative immunofluorescence on at least one series. All statistical tests were two-sided. We found no evidence for loss of antigenicity with time-to-fixation for ER, PgR, HER2, or Ki67 in a 4-hour time window. However, with a bootstrapping analysis, we observed a trend toward loss for ER and PgR, a statistically significant loss of antigenicity for phosphorylated tyrosine (P = .0048), and trends toward loss for other proteins. There was evidence of increased antigenicity in acetylated lysine, AKAP13 (P = .009), and HIF1A (P = .046), which are proteins known to be expressed in conditions of hypoxia. The loss of antigenicity for phosphorylated tyrosine and increase in expression of AKAP13, and HIF1A were confirmed in the biopsy/resection series. Key breast cancer biomarkers show no evidence of loss of antigenicity, although this dataset assesses the relatively short time beyond the 1-hour limit in recent guidelines. Other proteins show changes in antigenicity in both directions. Future studies that extend the time range and normalize for heterogeneity will provide more comprehensive information on preanalytic variation due to cold ischemic time.

A general statistical test for correlations in a finite-length time series.

PubMed

Hanson, Jeffery A; Yang, Haw

2008-06-07

The statistical properties of the autocorrelation function from a time series composed of independently and identically distributed stochastic variables has been studied. Analytical expressions for the autocorrelation function's variance have been derived. It has been found that two common ways of calculating the autocorrelation, moving-average and Fourier transform, exhibit different uncertainty characteristics. For periodic time series, the Fourier transform method is preferred because it gives smaller uncertainties that are uniform through all time lags. Based on these analytical results, a statistically robust method has been proposed to test the existence of correlations in a time series. The statistical test is verified by computer simulations and an application to single-molecule fluorescence spectroscopy is discussed.

Identification of ELF3 as an early transcriptional regulator of human urothelium.

PubMed

Böck, Matthias; Hinley, Jennifer; Schmitt, Constanze; Wahlicht, Tom; Kramer, Stefan; Southgate, Jennifer

2014-02-15

Despite major advances in high-throughput and computational modelling techniques, understanding of the mechanisms regulating tissue specification and differentiation in higher eukaryotes, particularly man, remains limited. Microarray technology has been explored exhaustively in recent years and several standard approaches have been established to analyse the resultant datasets on a genome-wide scale. Gene expression time series offer a valuable opportunity to define temporal hierarchies and gain insight into the regulatory relationships of biological processes. However, unless datasets are exactly synchronous, time points cannot be compared directly. Here we present a data-driven analysis of regulatory elements from a microarray time series that tracked the differentiation of non-immortalised normal human urothelial (NHU) cells grown in culture. The datasets were obtained by harvesting differentiating and control cultures from finite bladder- and ureter-derived NHU cell lines at different time points using two previously validated, independent differentiation-inducing protocols. Due to the asynchronous nature of the data, a novel ranking analysis approach was adopted whereby we compared changes in the amplitude of experiment and control time series to identify common regulatory elements. Our approach offers a simple, fast and effective ranking method for genes that can be applied to other time series. The analysis identified ELF3 as a candidate transcriptional regulator involved in human urothelial cytodifferentiation. Differentiation-associated expression of ELF3 was confirmed in cell culture experiments and by immunohistochemical demonstration in situ. The importance of ELF3 in urothelial differentiation was verified by knockdown in NHU cells, which led to reduced expression of FOXA1 and GRHL3 transcription factors in response to PPARγ activation. The consequences of this were seen in the repressed expression of late/terminal differentiation-associated uroplakin 3a gene expression and in the compromised development and regeneration of urothelial barrier function. Copyright © 2014 Elsevier Inc. All rights reserved.

Statistical properties of fluctuations of time series representing appearances of words in nationwide blog data and their applications: An example of modeling fluctuation scalings of nonstationary time series.

PubMed

Watanabe, Hayafumi; Sano, Yukie; Takayasu, Hideki; Takayasu, Misako

2016-11-01

To elucidate the nontrivial empirical statistical properties of fluctuations of a typical nonsteady time series representing the appearance of words in blogs, we investigated approximately 3×10^{9} Japanese blog articles over a period of six years and analyze some corresponding mathematical models. First, we introduce a solvable nonsteady extension of the random diffusion model, which can be deduced by modeling the behavior of heterogeneous random bloggers. Next, we deduce theoretical expressions for both the temporal and ensemble fluctuation scalings of this model, and demonstrate that these expressions can reproduce all empirical scalings over eight orders of magnitude. Furthermore, we show that the model can reproduce other statistical properties of time series representing the appearance of words in blogs, such as functional forms of the probability density and correlations in the total number of blogs. As an application, we quantify the abnormality of special nationwide events by measuring the fluctuation scalings of 1771 basic adjectives.

Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.

PubMed

Chen, Chi-Kan

2017-07-26

The identification of genetic regulatory networks (GRNs) provides insights into complex cellular processes. A class of recurrent neural networks (RNNs) captures the dynamics of GRN. Algorithms combining the RNN and machine learning schemes were proposed to reconstruct small-scale GRNs using gene expression time series. We present new GRN reconstruction methods with neural networks. The RNN is extended to a class of recurrent multilayer perceptrons (RMLPs) with latent nodes. Our methods contain two steps: the edge rank assignment step and the network construction step. The former assigns ranks to all possible edges by a recursive procedure based on the estimated weights of wires of RNN/RMLP (RE RNN /RE RMLP ), and the latter constructs a network consisting of top-ranked edges under which the optimized RNN simulates the gene expression time series. The particle swarm optimization (PSO) is applied to optimize the parameters of RNNs and RMLPs in a two-step algorithm. The proposed RE RNN -RNN and RE RMLP -RNN algorithms are tested on synthetic and experimental gene expression time series of small GRNs of about 10 genes. The experimental time series are from the studies of yeast cell cycle regulated genes and E. coli DNA repair genes. The unstable estimation of RNN using experimental time series having limited data points can lead to fairly arbitrary predicted GRNs. Our methods incorporate RNN and RMLP into a two-step structure learning procedure. Results show that the RE RMLP using the RMLP with a suitable number of latent nodes to reduce the parameter dimension often result in more accurate edge ranks than the RE RNN using the regularized RNN on short simulated time series. Combining by a weighted majority voting rule the networks derived by the RE RMLP -RNN using different numbers of latent nodes in step one to infer the GRN, the method performs consistently and outperforms published algorithms for GRN reconstruction on most benchmark time series. The framework of two-step algorithms can potentially incorporate with different nonlinear differential equation models to reconstruct the GRN.

A user-defined data type for the storage of time series data allowing efficient similarity screening.

PubMed

Sorokin, Anatoly; Selkov, Gene; Goryanin, Igor

2012-07-16

The volume of the experimentally measured time series data is rapidly growing, while storage solutions offering better data types than simple arrays of numbers or opaque blobs for keeping series data are sorely lacking. A number of indexing methods have been proposed to provide efficient access to time series data, but none has so far been integrated into a tried-and-proven database system. To explore the possibility of such integration, we have developed a data type for time series storage in PostgreSQL, an object-relational database system, and equipped it with an access method based on SAX (Symbolic Aggregate approXimation). This new data type has been successfully tested in a database supporting a large-scale plant gene expression experiment, and it was additionally tested on a very large set of simulated time series data. Copyright © 2011 Elsevier B.V. All rights reserved.

Investigation of Cepstrum Analysis for Seismic/Acoustic Signal Sensor Range Determination.

DTIC Science & Technology

1981-01-01

distorted by transmission through a linear system . For example, the effect of multipath and reverberation may be modeled in terms of a signal that is...called the short time averaged cepstrum. To derive some analytical expressions for short time average cepstrums we choose some functions of interest...linear process applied to the time series or any equivalent time function Repiod Period The amount of time required for one cycle of a time series Saphe

Quantitative evaluation of cross correlation between two finite-length time series with applications to single-molecule FRET.

PubMed

Hanson, Jeffery A; Yang, Haw

2008-11-06

The statistical properties of the cross correlation between two time series has been studied. An analytical expression for the cross correlation function's variance has been derived. On the basis of these results, a statistically robust method has been proposed to detect the existence and determine the direction of cross correlation between two time series. The proposed method has been characterized by computer simulations. Applications to single-molecule fluorescence spectroscopy are discussed. The results may also find immediate applications in fluorescence correlation spectroscopy (FCS) and its variants.

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

PubMed Central

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

PubMed

Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

PubMed

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Quantitative Assessment of Effect of Preanalytic Cold Ischemic Time on Protein Expression in Breast Cancer Tissues

PubMed Central

2012-01-01

Background Companion diagnostic tests can depend on accurate measurement of protein expression in tissues. Preanalytic variables, especially cold ischemic time (time from tissue removal to fixation in formalin) can affect the measurement and may cause false-negative results. We examined 23 proteins, including four commonly used breast cancer biomarker proteins, to quantify their sensitivity to cold ischemia in breast cancer tissues. Methods A series of 93 breast cancer specimens with known time-to-fixation represented in a tissue microarray and a second series of 25 matched pairs of core needle biopsies and breast cancer resections were used to evaluate changes in antigenicity as a function of cold ischemic time. Estrogen receptor (ER), progesterone receptor (PgR), HER2 or Ki67, and 19 other antigens were tested. Each antigen was measured using the AQUA method of quantitative immunofluorescence on at least one series. All statistical tests were two-sided. Results We found no evidence for loss of antigenicity with time-to-fixation for ER, PgR, HER2, or Ki67 in a 4-hour time window. However, with a bootstrapping analysis, we observed a trend toward loss for ER and PgR, a statistically significant loss of antigenicity for phosphorylated tyrosine (P = .0048), and trends toward loss for other proteins. There was evidence of increased antigenicity in acetylated lysine, AKAP13 (P = .009), and HIF1A (P = .046), which are proteins known to be expressed in conditions of hypoxia. The loss of antigenicity for phosphorylated tyrosine and increase in expression of AKAP13, and HIF1A were confirmed in the biopsy/resection series. Conclusions Key breast cancer biomarkers show no evidence of loss of antigenicity, although this dataset assesses the relatively short time beyond the 1-hour limit in recent guidelines. Other proteins show changes in antigenicity in both directions. Future studies that extend the time range and normalize for heterogeneity will provide more comprehensive information on preanalytic variation due to cold ischemic time. PMID:23090068

An Analytical Time–Domain Expression for the Net Ripple Produced by Parallel Interleaved Converters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Brian B.; Krein, Philip T.

We apply modular arithmetic and Fourier series to analyze the superposition of N interleaved triangular waveforms with identical amplitudes and duty-ratios. Here, interleaving refers to the condition when a collection of periodic waveforms with identical periods are each uniformly phase-shifted across one period. The main result is a time-domain expression which provides an exact representation of the summed and interleaved triangular waveforms, where the peak amplitude and parameters of the time-periodic component are all specified in closed-form. Analysis is general and can be used to study various applications in multi-converter systems. This model is unique not only in that itmore » reveals a simple and intuitive expression for the net ripple, but its derivation via modular arithmetic and Fourier series is distinct from prior approaches. The analytical framework is experimentally validated with a system of three parallel converters under time-varying operating conditions.« less

Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon.

PubMed

Koda, Satoru; Onda, Yoshihiko; Matsui, Hidetoshi; Takahagi, Kotaro; Yamaguchi-Uehara, Yukiko; Shimizu, Minami; Inoue, Komaki; Yoshida, Takuhiro; Sakurai, Tetsuya; Honda, Hiroshi; Eguchi, Shinto; Nishii, Ryuei; Mochida, Keiichi

2017-01-01

We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX) model with a group smoothly clipped absolute deviation (SCAD) method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon . To reveal the diurnal changes in the transcriptome in B. distachyon , we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon . On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon , aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.

The PEPR GeneChip data warehouse, and implementation of a dynamic time series query tool (SGQT) with graphical interface.

PubMed

Chen, Josephine; Zhao, Po; Massaro, Donald; Clerch, Linda B; Almon, Richard R; DuBois, Debra C; Jusko, William J; Hoffman, Eric P

2004-01-01

Publicly accessible DNA databases (genome browsers) are rapidly accelerating post-genomic research (see http://www.genome.ucsc.edu/), with integrated genomic DNA, gene structure, EST/ splicing and cross-species ortholog data. DNA databases have relatively low dimensionality; the genome is a linear code that anchors all associated data. In contrast, RNA expression and protein databases need to be able to handle very high dimensional data, with time, tissue, cell type and genes, as interrelated variables. The high dimensionality of microarray expression profile data, and the lack of a standard experimental platform have complicated the development of web-accessible databases and analytical tools. We have designed and implemented a public resource of expression profile data containing 1024 human, mouse and rat Affymetrix GeneChip expression profiles, generated in the same laboratory, and subject to the same quality and procedural controls (Public Expression Profiling Resource; PEPR). Our Oracle-based PEPR data warehouse includes a novel time series query analysis tool (SGQT), enabling dynamic generation of graphs and spreadsheets showing the action of any transcript of interest over time. In this report, we demonstrate the utility of this tool using a 27 time point, in vivo muscle regeneration series. This data warehouse and associated analysis tools provides access to multidimensional microarray data through web-based interfaces, both for download of all types of raw data for independent analysis, and also for straightforward gene-based queries. Planned implementations of PEPR will include web-based remote entry of projects adhering to quality control and standard operating procedure (QC/SOP) criteria, and automated output of alternative probe set algorithms for each project (see http://microarray.cnmcresearch.org/pgadatatable.asp).

The PEPR GeneChip data warehouse, and implementation of a dynamic time series query tool (SGQT) with graphical interface

PubMed Central

Chen, Josephine; Zhao, Po; Massaro, Donald; Clerch, Linda B.; Almon, Richard R.; DuBois, Debra C.; Jusko, William J.; Hoffman, Eric P.

2004-01-01

Publicly accessible DNA databases (genome browsers) are rapidly accelerating post-genomic research (see http://www.genome.ucsc.edu/), with integrated genomic DNA, gene structure, EST/ splicing and cross-species ortholog data. DNA databases have relatively low dimensionality; the genome is a linear code that anchors all associated data. In contrast, RNA expression and protein databases need to be able to handle very high dimensional data, with time, tissue, cell type and genes, as interrelated variables. The high dimensionality of microarray expression profile data, and the lack of a standard experimental platform have complicated the development of web-accessible databases and analytical tools. We have designed and implemented a public resource of expression profile data containing 1024 human, mouse and rat Affymetrix GeneChip expression profiles, generated in the same laboratory, and subject to the same quality and procedural controls (Public Expression Profiling Resource; PEPR). Our Oracle-based PEPR data warehouse includes a novel time series query analysis tool (SGQT), enabling dynamic generation of graphs and spreadsheets showing the action of any transcript of interest over time. In this report, we demonstrate the utility of this tool using a 27 time point, in vivo muscle regeneration series. This data warehouse and associated analysis tools provides access to multidimensional microarray data through web-based interfaces, both for download of all types of raw data for independent analysis, and also for straightforward gene-based queries. Planned implementations of PEPR will include web-based remote entry of projects adhering to quality control and standard operating procedure (QC/SOP) criteria, and automated output of alternative probe set algorithms for each project (see http://microarray.cnmcresearch.org/pgadatatable.asp). PMID:14681485

BATS: a Bayesian user-friendly software for analyzing time series microarray experiments.

PubMed

Angelini, Claudia; Cutillo, Luisa; De Canditiis, Daniela; Mutarelli, Margherita; Pensky, Marianna

2008-10-06

Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest. From methodological and computational point of view, analyzing high-dimensional time course microarray data requires very specific set of tools which are usually not included in standard software packages. Recently, the authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a 'one-sample' time-course microarray experiment, to rank them and to estimate their expression profiles. The method is based on explicit expressions for calculations and, hence, very computationally efficient. The software package BATS (Bayesian Analysis of Time Series) presented here implements the methodology described above. It allows an user to automatically identify and rank differentially expressed genes and to estimate their expression profiles when at least 5-6 time points are available. The package has a user-friendly interface. BATS successfully manages various technical difficulties which arise in time-course microarray experiments, such as a small number of observations, non-uniform sampling intervals and replicated or missing data. BATS is a free user-friendly software for the analysis of both simulated and real microarray time course experiments. The software, the user manual and a brief illustrative example are freely available online at the BATS website: http://www.na.iac.cnr.it/bats.

Transcriptional Analysis of Flowering Time in Switchgrass

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tornqvist, Carl-Erik; Vaillancourt, Brieanne; Kim, Jeongwoon

Over the past two decades, switchgrass (Panicum virgatum) has emerged as a priority biofuel feedstock. The bulk of switchgrass biomass is in the vegetative portion of the plant; therefore, increasing the length of vegetative growth will lead to an increase in overall biomass yield. The goal of this study was to gain insight into the control of flowering time in switchgrass that would assist in development of cultivars with longer vegetative phases through delayed flowering. RNA sequencing was used to assess genome-wide expression profiles across a developmental series between switchgrass genotypes belonging to the two main ecotypes: upland, typically earlymore » flowering, and lowland, typically late flowering. Leaf blades and tissues enriched for the shoot apical meristem (SAM) were collected in a developmental series from emergence through anthesis for RNA extraction. RNA from samples that flanked the SAM transition stage was sequenced for expression analyses. The analyses revealed differential expression patterns between early- and late-flowering genotypes for known flowering time orthologs. Namely, genes shown to play roles in photoperiod response and the circadian clock in other species were identified as potential candidates for regulating flowering time in the switchgrass genotypes analyzed. Based on their expression patterns, many of the differentially expressed genes could also be classified as putative promoters or repressors of flowering. The candidate genes presented here may be used to guide switchgrass improvement through marker-assisted breeding and/or transgenic or gene editing approaches.Over the past two decades, switchgrass (Panicum virgatum) has emerged as a priority biofuel feedstock. The bulk of switchgrass biomass is in the vegetative portion of the plant; therefore, increasing the length of vegetative growth will lead to an increase in overall biomass yield. The goal of this study was to gain insight into the control of flowering time in switchgrass that would assist in development of cultivars with longer vegetative phases through delayed flowering. RNA sequencing was used to assess genome-wide expression profiles across a developmental series between switchgrass genotypes belonging to the two main ecotypes: upland, typically early flowering, and lowland, typically late flowering. Leaf blades and tissues enriched for the shoot apical meristem (SAM) were collected in a developmental series from emergence through anthesis for RNA extraction. RNA from samples that flanked the SAM transition stage was sequenced for expression analyses. The analyses revealed differential expression patterns between early- and late-flowering genotypes for known flowering time orthologs. Namely, genes shown to play roles in photoperiod response and the circadian clock in other species were identified as potential candidates for regulating flowering time in the switchgrass genotypes analyzed. Based on their expression patterns, many of the differentially expressed genes could also be classified as putative promoters or repressors of flowering. The candidate genes presented here may then be used to guide switchgrass improvement through marker-assisted breeding and/or transgenic or gene editing approaches.« less

Transcriptional Analysis of Flowering Time in Switchgrass

DOE PAGES

Tornqvist, Carl-Erik; Vaillancourt, Brieanne; Kim, Jeongwoon; ...

2017-04-27

Over the past two decades, switchgrass (Panicum virgatum) has emerged as a priority biofuel feedstock. The bulk of switchgrass biomass is in the vegetative portion of the plant; therefore, increasing the length of vegetative growth will lead to an increase in overall biomass yield. The goal of this study was to gain insight into the control of flowering time in switchgrass that would assist in development of cultivars with longer vegetative phases through delayed flowering. RNA sequencing was used to assess genome-wide expression profiles across a developmental series between switchgrass genotypes belonging to the two main ecotypes: upland, typically earlymore » flowering, and lowland, typically late flowering. Leaf blades and tissues enriched for the shoot apical meristem (SAM) were collected in a developmental series from emergence through anthesis for RNA extraction. RNA from samples that flanked the SAM transition stage was sequenced for expression analyses. The analyses revealed differential expression patterns between early- and late-flowering genotypes for known flowering time orthologs. Namely, genes shown to play roles in photoperiod response and the circadian clock in other species were identified as potential candidates for regulating flowering time in the switchgrass genotypes analyzed. Based on their expression patterns, many of the differentially expressed genes could also be classified as putative promoters or repressors of flowering. The candidate genes presented here may be used to guide switchgrass improvement through marker-assisted breeding and/or transgenic or gene editing approaches.Over the past two decades, switchgrass (Panicum virgatum) has emerged as a priority biofuel feedstock. The bulk of switchgrass biomass is in the vegetative portion of the plant; therefore, increasing the length of vegetative growth will lead to an increase in overall biomass yield. The goal of this study was to gain insight into the control of flowering time in switchgrass that would assist in development of cultivars with longer vegetative phases through delayed flowering. RNA sequencing was used to assess genome-wide expression profiles across a developmental series between switchgrass genotypes belonging to the two main ecotypes: upland, typically early flowering, and lowland, typically late flowering. Leaf blades and tissues enriched for the shoot apical meristem (SAM) were collected in a developmental series from emergence through anthesis for RNA extraction. RNA from samples that flanked the SAM transition stage was sequenced for expression analyses. The analyses revealed differential expression patterns between early- and late-flowering genotypes for known flowering time orthologs. Namely, genes shown to play roles in photoperiod response and the circadian clock in other species were identified as potential candidates for regulating flowering time in the switchgrass genotypes analyzed. Based on their expression patterns, many of the differentially expressed genes could also be classified as putative promoters or repressors of flowering. The candidate genes presented here may then be used to guide switchgrass improvement through marker-assisted breeding and/or transgenic or gene editing approaches.« less

On the equivalence of case-crossover and time series methods in environmental epidemiology.

PubMed

Lu, Yun; Zeger, Scott L

2007-04-01

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.

An Iterative Time Windowed Signature Algorithm for Time Dependent Transcription Module Discovery

PubMed Central

Meng, Jia; Gao, Shou-Jiang; Huang, Yufei

2010-01-01

An algorithm for the discovery of time varying modules using genome-wide expression data is present here. When applied to large-scale time serious data, our method is designed to discover not only the transcription modules but also their timing information, which is rarely annotated by the existing approaches. Rather than assuming commonly defined time constant transcription modules, a module is depicted as a set of genes that are co-regulated during a specific period of time, i.e., a time dependent transcription module (TDTM). A rigorous mathematical definition of TDTM is provided, which is serve as an objective function for retrieving modules. Based on the definition, an effective signature algorithm is proposed that iteratively searches the transcription modules from the time series data. The proposed method was tested on the simulated systems and applied to the human time series microarray data during Kaposi's sarcoma-associated herpesvirus (KSHV) infection. The result has been verified by Expression Analysis Systematic Explorer. PMID:21552463

The genomic response of skeletal muscle to methylprednisolone using microarrays: tailoring data mining to the structure of the pharmacogenomic time series

PubMed Central

DuBois, Debra C; Piel, William H; Jusko, William J

2008-01-01

High-throughput data collection using gene microarrays has great potential as a method for addressing the pharmacogenomics of complex biological systems. Similarly, mechanism-based pharmacokinetic/pharmacodynamic modeling provides a tool for formulating quantitative testable hypotheses concerning the responses of complex biological systems. As the response of such systems to drugs generally entails cascades of molecular events in time, a time series design provides the best approach to capturing the full scope of drug effects. A major problem in using microarrays for high-throughput data collection is sorting through the massive amount of data in order to identify probe sets and genes of interest. Due to its inherent redundancy, a rich time series containing many time points and multiple samples per time point allows for the use of less stringent criteria of expression, expression change and data quality for initial filtering of unwanted probe sets. The remaining probe sets can then become the focus of more intense scrutiny by other methods, including temporal clustering, functional clustering and pharmacokinetic/pharmacodynamic modeling, which provide additional ways of identifying the probes and genes of pharmacological interest. PMID:15212590

Robotics and dynamic image analysis for studies of gene expression in plant tissues.

PubMed

Hernandez-Garcia, Carlos M; Chiera, Joseph M; Finer, John J

2010-05-05

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods presented here utilize the green fluorescent protein (gfp) gene for continual monitoring of gene expression in the same pieces of tissues, over time. The gfp gene was placed under regulatory control of different promoters and introduced into lima bean cotyledonary tissues via particle bombardment. Cotyledons were then placed on a robotic image collection system, which consisted of a fluorescence dissecting microscope with a digital camera and a 2-dimensional robotics platform custom-designed to allow secure attachment of culture dishes. Images were collected from cotyledonary tissues every hour for 100 hours to generate expression profiles for each promoter. Each collected series of 100 images was first subjected to manual image alignment using ImageReady to make certain that GFP-expressing foci were consistently retained within selected fields of analysis. Specific regions of the series measuring 300 x 400 pixels, were then selected for further analysis to provide GFP Intensity measurements using ImageJ software. Batch images were separated into the red, green and blue channels and GFP-expressing areas were identified using the threshold feature of ImageJ. After subtracting the background fluorescence (subtraction of gray values of non-expressing pixels from every pixel) in the respective red and green channels, GFP intensity was calculated by multiplying the mean grayscale value per pixel by the total number of GFP-expressing pixels in each channel, and then adding those values for both the red and green channels. GFP Intensity values were collected for all 100 time points to yield expression profiles. Variations in GFP expression profiles resulted from differences in factors such as promoter strength, presence of a silencing suppressor, or nature of the promoter. In addition to quantification of GFP intensity, the image series were also used to generate time-lapse animations using ImageReady. Time-lapse animations revealed that the clear majority of cells displayed a relatively rapid increase in GFP expression, followed by a slow decline. Some cells occasionally displayed a sudden loss of fluorescence, which may be associated with rapid cell death. Apparent transport of GFP across the membrane and cell wall to adjacent cells was also observed. Time lapse animations provided additional information that could not otherwise be obtained using GFP Intensity profiles or single time point image collections.

A method to identify differential expression profiles of time-course gene data with Fourier transformation.

PubMed

Kim, Jaehee; Ogden, Robert Todd; Kim, Haseong

2013-10-18

Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization.The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics.

Ince-Gaussian series representation of the two-dimensional fractional Fourier transform.

PubMed

Bandres, Miguel A; Gutiérrez-Vega, Julio C

2005-03-01

We introduce the Ince-Gaussian series representation of the two-dimensional fractional Fourier transform in elliptical coordinates. A physical interpretation is provided in terms of field propagation in quadratic graded-index media whose eigenmodes in elliptical coordinates are derived for the first time to our knowledge. The kernel of the new series representation is expressed in terms of Ince-Gaussian functions. The equivalence among the Hermite-Gaussian, Laguerre-Gaussian, and Ince-Gaussian series representations is verified by establishing the relation among the three definitions.

On power series representing solutions of the one-dimensional time-independent Schrödinger equation

NASA Astrophysics Data System (ADS)

Trotsenko, N. P.

2017-06-01

For the equation χ″( x) = u( x)χ( x) with infinitely smooth u( x), the general solution χ( x) is found in the form of a power series. The coefficients of the series are expressed via all derivatives u ( m)( y) of the function u( x) at a fixed point y. Examples of solutions for particular functions u( x) are considered.

Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.

PubMed

Hensman, James; Lawrence, Neil D; Rattray, Magnus

2013-08-20

Time course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications. We propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems. In particular, we illustrate the method's capacity for missing data imputation, data fusion and clustering.The method can impute data which is missing both systematically and at random: in a hold-out test on real data, performance is significantly better than commonly used imputation methods. The method's ability to model inter- and intra-cluster variance leads to more biologically meaningful clusters. The approach removes the necessity for evenly spaced samples, an advantage illustrated on a developmental Drosophila dataset with irregular replications. The hierarchical Gaussian process model provides an excellent statistical basis for several gene-expression time-series tasks. It has only a few additional parameters over a regular GP, has negligible additional complexity, is easily implemented and can be integrated into several existing algorithms. Our experiments were implemented in python, and are available from the authors' website: http://staffwww.dcs.shef.ac.uk/people/J.Hensman/.

Time-Series Analyses of Transcriptomes and Proteomes Reveal Molecular Networks Underlying Oil Accumulation in Canola.

PubMed

Wan, Huafang; Cui, Yixin; Ding, Yijuan; Mei, Jiaqin; Dong, Hongli; Zhang, Wenxin; Wu, Shiqi; Liang, Ying; Zhang, Chunyu; Li, Jiana; Xiong, Qing; Qian, Wei

2016-01-01

Understanding the regulation of lipid metabolism is vital for genetic engineering of canola ( Brassica napus L.) to increase oil yield or modify oil composition. We conducted time-series analyses of transcriptomes and proteomes to uncover the molecular networks associated with oil accumulation and dynamic changes in these networks in canola. The expression levels of genes and proteins were measured at 2, 4, 6, and 8 weeks after pollination (WAP). Our results show that the biosynthesis of fatty acids is a dominant cellular process from 2 to 6 WAP, while the degradation mainly happens after 6 WAP. We found that genes in almost every node of fatty acid synthesis pathway were significantly up-regulated during oil accumulation. Moreover, significant expression changes of two genes, acetyl-CoA carboxylase and acyl-ACP desaturase, were detected on both transcriptomic and proteomic levels. We confirmed the temporal expression patterns revealed by the transcriptomic analyses using quantitative real-time PCR experiments. The gene set association analysis show that the biosynthesis of fatty acids and unsaturated fatty acids are the most significant biological processes from 2-4 WAP and 4-6 WAP, respectively, which is consistent with the results of time-series analyses. These results not only provide insight into the mechanisms underlying lipid metabolism, but also reveal novel candidate genes that are worth further investigation for their values in the genetic engineering of canola.

Improving cluster-based missing value estimation of DNA microarray data.

PubMed

Brás, Lígia P; Menezes, José C

2007-06-01

We present a modification of the weighted K-nearest neighbours imputation method (KNNimpute) for missing values (MVs) estimation in microarray data based on the reuse of estimated data. The method was called iterative KNN imputation (IKNNimpute) as the estimation is performed iteratively using the recently estimated values. The estimation efficiency of IKNNimpute was assessed under different conditions (data type, fraction and structure of missing data) by the normalized root mean squared error (NRMSE) and the correlation coefficients between estimated and true values, and compared with that of other cluster-based estimation methods (KNNimpute and sequential KNN). We further investigated the influence of imputation on the detection of differentially expressed genes using SAM by examining the differentially expressed genes that are lost after MV estimation. The performance measures give consistent results, indicating that the iterative procedure of IKNNimpute can enhance the prediction ability of cluster-based methods in the presence of high missing rates, in non-time series experiments and in data sets comprising both time series and non-time series data, because the information of the genes having MVs is used more efficiently and the iterative procedure allows refining the MV estimates. More importantly, IKNN has a smaller detrimental effect on the detection of differentially expressed genes.

Empathy, but not mimicry restriction, influences the recognition of change in emotional facial expressions.

PubMed

Kosonogov, Vladimir; Titova, Alisa; Vorobyeva, Elena

2015-01-01

The current study addressed the hypothesis that empathy and the restriction of facial muscles of observers can influence recognition of emotional facial expressions. A sample of 74 participants recognized the subjective onset of emotional facial expressions (anger, disgust, fear, happiness, sadness, surprise, and neutral) in a series of morphed face photographs showing a gradual change (frame by frame) from one expression to another. The high-empathy (as measured by the Empathy Quotient) participants recognized emotional facial expressions at earlier photographs from the series than did low-empathy ones, but there was no difference in the exploration time. Restriction of facial muscles of observers (with plasters and a stick in mouth) did not influence the responses. We discuss these findings in the context of the embodied simulation theory and previous data on empathy.

Transcriptome dynamics during the transition from anaerobic photosynthesis to aerobic respiration in Rhodobacter sphaeroides 2.4.1.

PubMed

Arai, Hiroyuki; Roh, Jung Hyeob; Kaplan, Samuel

2008-01-01

Rhodobacter sphaeroides 2.4.1 is a facultative photosynthetic anaerobe that grows by anoxygenic photosynthesis under anaerobic-light conditions. Changes in energy generation pathways under photosynthetic and aerobic respiratory conditions are primarily controlled by oxygen tensions. In this study, we performed time series microarray analyses to investigate transcriptome dynamics during the transition from anaerobic photosynthesis to aerobic respiration. Major changes in gene expression profiles occurred in the initial 15 min after the shift from anaerobic-light to aerobic-dark conditions, with changes continuing to occur up to 4 hours postshift. Those genes whose expression levels changed significantly during the time series were grouped into three major classes by clustering analysis. Class I contained genes, such as that for the aa3 cytochrome oxidase, whose expression levels increased after the shift. Class II contained genes, such as those for the photosynthetic apparatus and Calvin cycle enzymes, whose expression levels decreased after the shift. Class III contained genes whose expression levels temporarily increased during the time series. Many genes for metabolism and transport of carbohydrates or lipids were significantly induced early during the transition, suggesting that those endogenous compounds were initially utilized as carbon sources. Oxidation of those compounds might also be required for maintenance of redox homeostasis after exposure to oxygen. Genes for the repair of protein and sulfur groups and uptake of ferric iron were temporarily upregulated soon after the shift, suggesting they were involved in a response to oxidative stress. The flagellar-biosynthesis genes were expressed in a hierarchical manner at 15 to 60 min after the shift. Numerous transporters were induced at various time points, suggesting that the cellular composition went through significant changes during the transition from anaerobic photosynthesis to aerobic respiration. Analyses of these data make it clear that numerous regulatory activities come into play during the transition from one homeostatic state to another.

Genetic network inference as a series of discrimination tasks.

PubMed

Kimura, Shuhei; Nakayama, Satoshi; Hatakeyama, Mariko

2009-04-01

Genetic network inference methods based on sets of differential equations generally require a great deal of time, as the equations must be solved many times. To reduce the computational cost, researchers have proposed other methods for inferring genetic networks by solving sets of differential equations only a few times, or even without solving them at all. When we try to obtain reasonable network models using these methods, however, we must estimate the time derivatives of the gene expression levels with great precision. In this study, we propose a new method to overcome the drawbacks of inference methods based on sets of differential equations. Our method infers genetic networks by obtaining classifiers capable of predicting the signs of the derivatives of the gene expression levels. For this purpose, we defined a genetic network inference problem as a series of discrimination tasks, then solved the defined series of discrimination tasks with a linear programming machine. Our experimental results demonstrated that the proposed method is capable of correctly inferring genetic networks, and doing so more than 500 times faster than the other inference methods based on sets of differential equations. Next, we applied our method to actual expression data of the bacterial SOS DNA repair system. And finally, we demonstrated that our approach relates to the inference method based on the S-system model. Though our method provides no estimation of the kinetic parameters, it should be useful for researchers interested only in the network structure of a target system. Supplementary data are available at Bioinformatics online.

Assessing co-regulation of directly linked genes in biological networks using microarray time series analysis.

PubMed

Del Sorbo, Maria Rosaria; Balzano, Walter; Donato, Michele; Draghici, Sorin

2013-11-01

Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a β factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to -1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the β factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools, indicating the frequency approach as the best similarity metric among the three, for its ability to detect the correlation based on the correspondence of the most significant frequency components. Copyright © 2013. Published by Elsevier Ireland Ltd.

Time series models on analysing mortality rates and acute childhood lymphoid leukaemia.

PubMed

Kis, Maria

2005-01-01

In this paper we demonstrate applying time series models on medical research. The Hungarian mortality rates were analysed by autoregressive integrated moving average models and seasonal time series models examined the data of acute childhood lymphoid leukaemia.The mortality data may be analysed by time series methods such as autoregressive integrated moving average (ARIMA) modelling. This method is demonstrated by two examples: analysis of the mortality rates of ischemic heart diseases and analysis of the mortality rates of cancer of digestive system. Mathematical expressions are given for the results of analysis. The relationships between time series of mortality rates were studied with ARIMA models. Calculations of confidence intervals for autoregressive parameters by tree methods: standard normal distribution as estimation and estimation of the White's theory and the continuous time case estimation. Analysing the confidence intervals of the first order autoregressive parameters we may conclude that the confidence intervals were much smaller than other estimations by applying the continuous time estimation model.We present a new approach to analysing the occurrence of acute childhood lymphoid leukaemia. We decompose time series into components. The periodicity of acute childhood lymphoid leukaemia in Hungary was examined using seasonal decomposition time series method. The cyclic trend of the dates of diagnosis revealed that a higher percent of the peaks fell within the winter months than in the other seasons. This proves the seasonal occurrence of the childhood leukaemia in Hungary.

Systematic identification of an integrative network module during senescence from time-series gene expression.

PubMed

Park, Chihyun; Yun, So Jeong; Ryu, Sung Jin; Lee, Soyoung; Lee, Young-Sam; Yoon, Youngmi; Park, Sang Chul

2017-03-15

Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process. We suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator. Heretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches.

Final technical report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edward DeLong

2011-10-07

Our overarching goals in this project were to: Develop and improve high-throughput sequencing methods and analytical approaches for quantitative analyses of microbial gene expression at the Hawaii Ocean Time Series Station and the Bermuda Atlantic Time Series Station; Conduct field analyses following gene expression patterns in picoplankton microbial communities in general, and Prochlorococcus flow sorted from that community, as they respond to different environmental variables (light, macronutrients, dissolved organic carbon), that are predicted to influence activity, productivity, and carbon cycling; Use the expression analyses of flow sorted Prochlorococcus to identify horizontally transferred genes and gene products, in particular those thatmore » are located in genomic islands and likely to confer habitat-specific fitness advantages; Use the microbial community gene expression data that we generate to gain insights, and test hypotheses, about the variability, genomic context, activity and function of as yet uncharacterized gene products, that appear highly expressed in the environment. We achieved the above goals, and even more over the course of the project. This includes a number of novel methodological developments, as well as the standardization of microbial community gene expression analyses in both field surveys, and experimental modalities. The availability of these methods, tools and approaches is changing current practice in microbial community analyses.« less

The Earth Observation Monitor - Automated monitoring and alerting for spatial time-series data based on OGC web services

NASA Astrophysics Data System (ADS)

Eberle, J.; Hüttich, C.; Schmullius, C.

2014-12-01

Spatial time series data are freely available around the globe from earth observation satellites and meteorological stations for many years until now. They provide useful and important information to detect ongoing changes of the environment; but for end-users it is often too complex to extract this information out of the original time series datasets. This issue led to the development of the Earth Observation Monitor (EOM), an operational framework and research project to provide simple access, analysis and monitoring tools for global spatial time series data. A multi-source data processing middleware in the backend is linked to MODIS data from Land Processes Distributed Archive Center (LP DAAC) and Google Earth Engine as well as daily climate station data from NOAA National Climatic Data Center. OGC Web Processing Services are used to integrate datasets from linked data providers or external OGC-compliant interfaces to the EOM. Users can either use the web portal (webEOM) or the mobile application (mobileEOM) to execute these processing services and to retrieve the requested data for a given point or polygon in userfriendly file formats (CSV, GeoTiff). Beside providing just data access tools, users can also do further time series analyses like trend calculations, breakpoint detections or the derivation of phenological parameters from vegetation time series data. Furthermore data from climate stations can be aggregated over a given time interval. Calculated results can be visualized in the client and downloaded for offline usage. Automated monitoring and alerting of the time series data integrated by the user is provided by an OGC Sensor Observation Service with a coupled OGC Web Notification Service. Users can decide which datasets and parameters are monitored with a given filter expression (e.g., precipitation value higher than x millimeter per day, occurrence of a MODIS Fire point, detection of a time series anomaly). Datasets integrated in the SOS service are updated in near-realtime based on the linked data providers mentioned above. An alert is automatically pushed to the user if the new data meets the conditions of the registered filter expression. This monitoring service is available on the web portal with alerting by email and within the mobile app with alerting by email and push notification.

Transient electromagnetic scattering by a radially uniaxial dielectric sphere: Debye series, Mie series and ray tracing methods

NASA Astrophysics Data System (ADS)

Yazdani, Mohsen

Transient electromagnetic scattering by a radially uniaxial dielectric sphere is explored using three well-known methods: Debye series, Mie series, and ray tracing theory. In the first approach, the general solutions for the impulse and step responses of a uniaxial sphere are evaluated using the inverse Laplace transformation of the generalized Mie series solution. Following high frequency scattering solution of a large uniaxial sphere, the Mie series summation is split into the high frequency (HF) and low frequency terms where the HF term is replaced by its asymptotic expression allowing a significant reduction in computation time of the numerical Bromwich integral. In the second approach, the generalized Debye series for a radially uniaxial dielectric sphere is introduced and the Mie series coefficients are replaced by their equivalent Debye series formulations. The results are then applied to examine the transient response of each individual Debye term allowing the identification of impulse returns in the transient response of the uniaxial sphere. In the third approach, the ray tracing theory in a uniaxial sphere is investigated to evaluate the propagation path as well as the arrival time of the ordinary and extraordinary returns in the transient response of the uniaxial sphere. This is achieved by extracting the reflection and transmission angles of a plane wave obliquely incident on the radially oriented air-uniaxial and uniaxial-air boundaries, and expressing the phase velocities as well as the refractive indices of the ordinary and extraordinary waves in terms of the incident angle, optic axis and propagation direction. The results indicate a satisfactory agreement between Debye series, Mie series and ray tracing methods.

Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.

PubMed

Gonçalves, Joana P; Aires, Ricardo S; Francisco, Alexandre P; Madeira, Sara C

2012-01-01

Explaining regulatory mechanisms is crucial to understand complex cellular responses leading to system perturbations. Some strategies reverse engineer regulatory interactions from experimental data, while others identify functional regulatory units (modules) under the assumption that biological systems yield a modular organization. Most modular studies focus on network structure and static properties, ignoring that gene regulation is largely driven by stimulus-response behavior. Expression time series are key to gain insight into dynamics, but have been insufficiently explored by current methods, which often (1) apply generic algorithms unsuited for expression analysis over time, due to inability to maintain the chronology of events or incorporate time dependency; (2) ignore local patterns, abundant in most interesting cases of transcriptional activity; (3) neglect physical binding or lack automatic association of regulators, focusing mainly on expression patterns; or (4) limit the discovery to a predefined number of modules. We propose Regulatory Snapshots, an integrative mining approach to identify regulatory modules over time by combining transcriptional control with response, while overcoming the above challenges. Temporal biclustering is first used to reveal transcriptional modules composed of genes showing coherent expression profiles over time. Personalized ranking is then applied to prioritize prominent regulators targeting the modules at each time point using a network of documented regulatory associations and the expression data. Custom graphics are finally depicted to expose the regulatory activity in a module at consecutive time points (snapshots). Regulatory Snapshots successfully unraveled modules underlying yeast response to heat shock and human epithelial-to-mesenchymal transition, based on regulations documented in the YEASTRACT and JASPAR databases, respectively, and available expression data. Regulatory players involved in functionally enriched processes related to these biological events were identified. Ranking scores further suggested ability to discern the primary role of a gene (target or regulator). Prototype is available at: http://kdbio.inesc-id.pt/software/regulatorysnapshots.

Regulatory Snapshots: Integrative Mining of Regulatory Modules from Expression Time Series and Regulatory Networks

PubMed Central

Gonçalves, Joana P.; Aires, Ricardo S.; Francisco, Alexandre P.; Madeira, Sara C.

2012-01-01

Explaining regulatory mechanisms is crucial to understand complex cellular responses leading to system perturbations. Some strategies reverse engineer regulatory interactions from experimental data, while others identify functional regulatory units (modules) under the assumption that biological systems yield a modular organization. Most modular studies focus on network structure and static properties, ignoring that gene regulation is largely driven by stimulus-response behavior. Expression time series are key to gain insight into dynamics, but have been insufficiently explored by current methods, which often (1) apply generic algorithms unsuited for expression analysis over time, due to inability to maintain the chronology of events or incorporate time dependency; (2) ignore local patterns, abundant in most interesting cases of transcriptional activity; (3) neglect physical binding or lack automatic association of regulators, focusing mainly on expression patterns; or (4) limit the discovery to a predefined number of modules. We propose Regulatory Snapshots, an integrative mining approach to identify regulatory modules over time by combining transcriptional control with response, while overcoming the above challenges. Temporal biclustering is first used to reveal transcriptional modules composed of genes showing coherent expression profiles over time. Personalized ranking is then applied to prioritize prominent regulators targeting the modules at each time point using a network of documented regulatory associations and the expression data. Custom graphics are finally depicted to expose the regulatory activity in a module at consecutive time points (snapshots). Regulatory Snapshots successfully unraveled modules underlying yeast response to heat shock and human epithelial-to-mesenchymal transition, based on regulations documented in the YEASTRACT and JASPAR databases, respectively, and available expression data. Regulatory players involved in functionally enriched processes related to these biological events were identified. Ranking scores further suggested ability to discern the primary role of a gene (target or regulator). Prototype is available at: http://kdbio.inesc-id.pt/software/regulatorysnapshots. PMID:22563474

Spatio-temporal Event Classification using Time-series Kernel based Structured Sparsity

PubMed Central

Jeni, László A.; Lőrincz, András; Szabó, Zoltán; Cohn, Jeffrey F.; Kanade, Takeo

2016-01-01

In many behavioral domains, such as facial expression and gesture, sparse structure is prevalent. This sparsity would be well suited for event detection but for one problem. Features typically are confounded by alignment error in space and time. As a consequence, high-dimensional representations such as SIFT and Gabor features have been favored despite their much greater computational cost and potential loss of information. We propose a Kernel Structured Sparsity (KSS) method that can handle both the temporal alignment problem and the structured sparse reconstruction within a common framework, and it can rely on simple features. We characterize spatio-temporal events as time-series of motion patterns and by utilizing time-series kernels we apply standard structured-sparse coding techniques to tackle this important problem. We evaluated the KSS method using both gesture and facial expression datasets that include spontaneous behavior and differ in degree of difficulty and type of ground truth coding. KSS outperformed both sparse and non-sparse methods that utilize complex image features and their temporal extensions. In the case of early facial event classification KSS had 10% higher accuracy as measured by F1 score over kernel SVM methods1. PMID:27830214

Dynamic modelling of microRNA regulation during mesenchymal stem cell differentiation.

PubMed

Weber, Michael; Sotoca, Ana M; Kupfer, Peter; Guthke, Reinhard; van Zoelen, Everardus J

2013-11-12

Network inference from gene expression data is a typical approach to reconstruct gene regulatory networks. During chondrogenic differentiation of human mesenchymal stem cells (hMSCs), a complex transcriptional network is active and regulates the temporal differentiation progress. As modulators of transcriptional regulation, microRNAs (miRNAs) play a critical role in stem cell differentiation. Integrated network inference aimes at determining interrelations between miRNAs and mRNAs on the basis of expression data as well as miRNA target predictions. We applied the NetGenerator tool in order to infer an integrated gene regulatory network. Time series experiments were performed to measure mRNA and miRNA abundances of TGF-beta1+BMP2 stimulated hMSCs. Network nodes were identified by analysing temporal expression changes, miRNA target gene predictions, time series correlation and literature knowledge. Network inference was performed using NetGenerator to reconstruct a dynamical regulatory model based on the measured data and prior knowledge. The resulting model is robust against noise and shows an optimal trade-off between fitting precision and inclusion of prior knowledge. It predicts the influence of miRNAs on the expression of chondrogenic marker genes and therefore proposes novel regulatory relations in differentiation control. By analysing the inferred network, we identified a previously unknown regulatory effect of miR-524-5p on the expression of the transcription factor SOX9 and the chondrogenic marker genes COL2A1, ACAN and COL10A1. Genome-wide exploration of miRNA-mRNA regulatory relationships is a reasonable approach to identify miRNAs which have so far not been associated with the investigated differentiation process. The NetGenerator tool is able to identify valid gene regulatory networks on the basis of miRNA and mRNA time series data.

In search of functional association from time-series microarray data based on the change trend and level of gene expression

PubMed Central

He, Feng; Zeng, An-Ping

2006-01-01

Background The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. Results In this work we present a new method based on extracting main features of the change trend and level of gene expression between consecutive time points. The method, termed as trend correlation (TC), includes two major steps: 1, calculating a maximal local alignment of change trend score by dynamic programming and a change trend correlation coefficient between the maximal matched change levels of each gene pair; 2, inferring relationships of gene pairs based on two statistical extraction procedures. The new method considers time shifts and inverted relationships in a similar way as the local clustering (LC) method but the latter is merely based on a point-to-point comparison. The TC method is demonstrated with data from yeast cell cycle and compared with the LC method and the widely used Pearson correlation coefficient (PCC) based clustering method. The biological significance of the gene pairs is examined with several large-scale yeast databases. Although the TC method predicts an overall lower number of gene pairs than the other two methods at a same p-value threshold, the additional number of gene pairs inferred by the TC method is considerable: e.g. 20.5% compared with the LC method and 49.6% with the PCC method for a p-value threshold of 2.7E-3. Moreover, the percentage of the inferred gene pairs consistent with databases by our method is generally higher than the LC method and similar to the PCC method. A significant number of the gene pairs only inferred by the TC method are process-identity or function-similarity pairs or have well-documented biological interactions, including 443 known protein interactions and some known cell cycle related regulatory interactions. It should be emphasized that the overlapping of gene pairs detected by the three methods is normally not very high, indicating a necessity of combining the different methods in search of functional association of genes from time-series data. For a p-value threshold of 1E-5 the percentage of process-identity and function-similarity gene pairs among the shared part of the three methods reaches 60.2% and 55.6% respectively, building a good basis for further experimental and functional study. Furthermore, the combined use of methods is important to infer more complete regulatory circuits and network as exemplified in this study. Conclusion The TC method can significantly augment the current major methods to infer functional linkages and biological network and is well suitable for exploring temporal relationships of gene expression in time-series data. PMID:16478547

Reverse engineering gene regulatory networks from measurement with missing values.

PubMed

Ogundijo, Oyetunji E; Elmas, Abdulkadir; Wang, Xiaodong

2016-12-01

Gene expression time series data are usually in the form of high-dimensional arrays. Unfortunately, the data may sometimes contain missing values: for either the expression values of some genes at some time points or the entire expression values of a single time point or some sets of consecutive time points. This significantly affects the performance of many algorithms for gene expression analysis that take as an input, the complete matrix of gene expression measurement. For instance, previous works have shown that gene regulatory interactions can be estimated from the complete matrix of gene expression measurement. Yet, till date, few algorithms have been proposed for the inference of gene regulatory network from gene expression data with missing values. We describe a nonlinear dynamic stochastic model for the evolution of gene expression. The model captures the structural, dynamical, and the nonlinear natures of the underlying biomolecular systems. We present point-based Gaussian approximation (PBGA) filters for joint state and parameter estimation of the system with one-step or two-step missing measurements . The PBGA filters use Gaussian approximation and various quadrature rules, such as the unscented transform (UT), the third-degree cubature rule and the central difference rule for computing the related posteriors. The proposed algorithm is evaluated with satisfying results for synthetic networks, in silico networks released as a part of the DREAM project, and the real biological network, the in vivo reverse engineering and modeling assessment (IRMA) network of yeast Saccharomyces cerevisiae . PBGA filters are proposed to elucidate the underlying gene regulatory network (GRN) from time series gene expression data that contain missing values. In our state-space model, we proposed a measurement model that incorporates the effect of the missing data points into the sequential algorithm. This approach produces a better inference of the model parameters and hence, more accurate prediction of the underlying GRN compared to when using the conventional Gaussian approximation (GA) filters ignoring the missing data points.

Genetic Networks and Anticipation of Gene Expression Patterns

NASA Astrophysics Data System (ADS)

Gebert, J.; Lätsch, M.; Pickl, S. W.; Radde, N.; Weber, G.-W.; Wünschiers, R.

2004-08-01

An interesting problem for computational biology is the analysis of time-series expression data. Here, the application of modern methods from dynamical systems, optimization theory, numerical algorithms and the utilization of implicit discrete information lead to a deeper understanding. In [1], we suggested to represent the behavior of time-series gene expression patterns by a system of ordinary differential equations, which we analytically and algorithmically investigated under the parametrical aspect of stability or instability. Our algorithm strongly exploited combinatorial information. In this paper, we deepen, extend and exemplify this study from the viewpoint of underlying mathematical modelling. This modelling consists in evaluating DNA-microarray measurements as the basis of anticipatory prediction, in the choice of a smooth model given by differential equations, in an approach of the right-hand side with parametric matrices, and in a discrete approximation which is a least squares optimization problem. We give a mathematical and biological discussion, and pay attention to the special case of a linear system, where the matrices do not depend on the state of expressions. Here, we present first numerical examples.

Temporal and long-term trend analysis of class C notifiable diseases in China from 2009 to 2014

PubMed Central

Zhang, Xingyu; Hou, Fengsu; Qiao, Zhijiao; Li, Xiaosong; Zhou, Lijun; Liu, Yuanyuan; Zhang, Tao

2016-01-01

Objectives Time series models are effective tools for disease forecasting. This study aims to explore the time series behaviour of 11 notifiable diseases in China and to predict their incidence through effective models. Settings and participants The Chinese Ministry of Health started to publish class C notifiable diseases in 2009. The monthly reported case time series of 11 infectious diseases from the surveillance system between 2009 and 2014 was collected. Methods We performed a descriptive and a time series study using the surveillance data. Decomposition methods were used to explore (1) their seasonality expressed in the form of seasonal indices and (2) their long-term trend in the form of a linear regression model. Autoregressive integrated moving average (ARIMA) models have been established for each disease. Results The number of cases and deaths caused by hand, foot and mouth disease ranks number 1 among the detected diseases. It occurred most often in May and July and increased, on average, by 0.14126/100 000 per month. The remaining incidence models show good fit except the influenza and hydatid disease models. Both the hydatid disease and influenza series become white noise after differencing, so no available ARIMA model can be fitted for these two diseases. Conclusion Time series analysis of effective surveillance time series is useful for better understanding the occurrence of the 11 types of infectious disease. PMID:27797981

A method to identify differential expression profiles of time-course gene data with Fourier transformation

PubMed Central

2013-01-01

Background Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. Results This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization. The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Conclusions Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics. PMID:24134721

Genexpi: a toolset for identifying regulons and validating gene regulatory networks using time-course expression data.

PubMed

Modrák, Martin; Vohradský, Jiří

2018-04-13

Identifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks. We introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package. Genexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.

Early-Time Solution of the Horizontal Unconfined Aquifer in the Buildup Phase

NASA Astrophysics Data System (ADS)

Gravanis, Elias; Akylas, Evangelos

2017-10-01

We derive the early-time solution of the Boussinesq equation for the horizontal unconfined aquifer in the buildup phase under constant recharge and zero inflow. The solution is expressed as a power series of a suitable similarity variable, which is constructed so that to satisfy the boundary conditions at both ends of the aquifer, that is, it is a polynomial approximation of the exact solution. The series turns out to be asymptotic and it is regularized by resummation techniques that are used to define divergent series. The outflow rate in this regime is linear in time, and the (dimensionless) coefficient is calculated to eight significant figures. The local error of the series is quantified by its deviation from satisfying the self-similar Boussinesq equation at every point. The local error turns out to be everywhere positive, hence, so is the integrated error, which in turn quantifies the degree of convergence of the series to the exact solution.

Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches.

PubMed

Oh, Sunghee; Song, Seongho

2017-01-01

In gene expression profile, data analysis pipeline is categorized into four levels, major downstream tasks, i.e., (1) identification of differential expression; (2) clustering co-expression patterns; (3) classification of subtypes of samples; and (4) detection of genetic regulatory networks, are performed posterior to preprocessing procedure such as normalization techniques. To be more specific, temporal dynamic gene expression data has its inherent feature, namely, two neighboring time points (previous and current state) are highly correlated with each other, compared to static expression data which samples are assumed as independent individuals. In this chapter, we demonstrate how HMMs and hierarchical Bayesian modeling methods capture the horizontal time dependency structures in time series expression profiles by focusing on the identification of differential expression. In addition, those differential expression genes and transcript variant isoforms over time detected in core prerequisite steps can be generally further applied in detection of genetic regulatory networks to comprehensively uncover dynamic repertoires in the aspects of system biology as the coupled framework.

A univariate model of river water nitrate time series

NASA Astrophysics Data System (ADS)

Worrall, F.; Burt, T. P.

1999-01-01

Four time series were taken from three catchments in the North and South of England. The sites chosen included two in predominantly agricultural catchments, one at the tidal limit and one downstream of a sewage treatment works. A time series model was constructed for each of these series as a means of decomposing the elements controlling river water nitrate concentrations and to assess whether this approach could provide a simple management tool for protecting water abstractions. Autoregressive (AR) modelling of the detrended and deseasoned time series showed a "memory effect". This memory effect expressed itself as an increase in the winter-summer difference in nitrate levels that was dependent upon the nitrate concentration 12 or 6 months previously. Autoregressive moving average (ARMA) modelling showed that one of the series contained seasonal, non-stationary elements that appeared as an increasing trend in the winter-summer difference. The ARMA model was used to predict nitrate levels and predictions were tested against data held back from the model construction process - predictions gave average percentage errors of less than 10%. Empirical modelling can therefore provide a simple, efficient method for constructing management models for downstream water abstraction.

Interpretable Early Classification of Multivariate Time Series

ERIC Educational Resources Information Center

Ghalwash, Mohamed F.

2013-01-01

Recent advances in technology have led to an explosion in data collection over time rather than in a single snapshot. For example, microarray technology allows us to measure gene expression levels in different conditions over time. Such temporal data grants the opportunity for data miners to develop algorithms to address domain-related problems,…

Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways

PubMed Central

Jo, Kyuri; Jung, Inuk; Moon, Ji Hwan; Kim, Sun

2016-01-01

Motivation: To understand the dynamic nature of the biological process, it is crucial to identify perturbed pathways in an altered environment and also to infer regulators that trigger the response. Current time-series analysis methods, however, are not powerful enough to identify perturbed pathways and regulators simultaneously. Widely used methods include methods to determine gene sets such as differentially expressed genes or gene clusters and these genes sets need to be further interpreted in terms of biological pathways using other tools. Most pathway analysis methods are not designed for time series data and they do not consider gene-gene influence on the time dimension. Results: In this article, we propose a novel time-series analysis method TimeTP for determining transcription factors (TFs) regulating pathway perturbation, which narrows the focus to perturbed sub-pathways and utilizes the gene regulatory network and protein–protein interaction network to locate TFs triggering the perturbation. TimeTP first identifies perturbed sub-pathways that propagate the expression changes along the time. Starting points of the perturbed sub-pathways are mapped into the network and the most influential TFs are determined by influence maximization technique. The analysis result is visually summarized in TF-Pathway map in time clock. TimeTP was applied to PIK3CA knock-in dataset and found significant sub-pathways and their regulators relevant to the PIP3 signaling pathway. Availability and Implementation: TimeTP is implemented in Python and available at http://biohealth.snu.ac.kr/software/TimeTP/. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: sunkim.bioinfo@snu.ac.kr PMID:27307609

The complexity of gene expression dynamics revealed by permutation entropy

PubMed Central

2010-01-01

Background High complexity is considered a hallmark of living systems. Here we investigate the complexity of temporal gene expression patterns using the concept of Permutation Entropy (PE) first introduced in dynamical systems theory. The analysis of gene expression data has so far focused primarily on the identification of differentially expressed genes, or on the elucidation of pathway and regulatory relationships. We aim to study gene expression time series data from the viewpoint of complexity. Results Applying the PE complexity metric to abiotic stress response time series data in Arabidopsis thaliana, genes involved in stress response and signaling were found to be associated with the highest complexity not only under stress, but surprisingly, also under reference, non-stress conditions. Genes with house-keeping functions exhibited lower PE complexity. Compared to reference conditions, the PE of temporal gene expression patterns generally increased upon stress exposure. High-complexity genes were found to have longer upstream intergenic regions and more cis-regulatory motifs in their promoter regions indicative of a more complex regulatory apparatus needed to orchestrate their expression, and to be associated with higher correlation network connectivity degree. Arabidopsis genes also present in other plant species were observed to exhibit decreased PE complexity compared to Arabidopsis specific genes. Conclusions We show that Permutation Entropy is a simple yet robust and powerful approach to identify temporal gene expression profiles of varying complexity that is equally applicable to other types of molecular profile data. PMID:21176199

Analysis of the precipitation and streamflow extremes in Northern Italy using high resolution reanalysis dataset Express-Hydro

NASA Astrophysics Data System (ADS)

Silvestro, Francesco; Parodi, Antonio; Campo, Lorenzo

2017-04-01

The characterization of the hydrometeorological extremes, both in terms of rainfall and streamflow, in a given region plays a key role in the environmental monitoring provided by the flood alert services. In last years meteorological simulations (both near real-time and historical reanalysis) were available at increasing spatial and temporal resolutions, making possible long-period hydrological reanalysis in which the meteo dataset is used as input in distributed hydrological models. In this work, a very high resolution meteorological reanalysis dataset, namely Express-Hydro (CIMA, ISAC-CNR, GAUSS Special Project PR45DE), was employed as input in the hydrological model Continuum in order to produce long time series of streamflows in the Liguria territory, located in the Northern part of Italy. The original dataset covers the whole Europe territory in the 1979-2008 period, at 4 km of spatial resolution and 3 hours of time resolution. Analyses in terms of comparison between the rainfall estimated by the dataset and the observations (available from the local raingauges network) were carried out, and a bias correction was also performed in order to better match the observed climatology. An extreme analysis was eventually carried on the streamflows time series obtained by the simulations, by comparing them with the results of the same hydrological model fed with the observed time series of rainfall. The results of the analysis are shown and discussed.

Estimating the effective spatial resolution of an AVHRR time series

USGS Publications Warehouse

Meyer, D.J.

1996-01-01

A method is proposed to estimate the spatial degradation of geometrically rectified AVHRR data resulting from misregistration and off-nadir viewing, and to infer the cumulative effect of these degradations over time. Misregistrations are measured using high resolution imagery as a geometric reference, and pixel sizes are computed directly from satellite zenith angles. The influence or neighbouring features on a nominal 1 km by 1 km pixel over a given site is estimated from the above information, and expressed as a spatial distribution whose spatial frequency response is used to define an effective field-of-view (EFOV) for a time series. In a demonstration of the technique applied to images from the Conterminous U.S. AVHRR data set, an EFOV of 3·1km in the east-west dimension and 19 km in the north-south dimension was estimated for a time series accumulated over a grasslands test site.

Analysis and stochastic modelling of Intensity-Duration-Frequency relationship from 88 years of 10 min rainfall data in North Spain

NASA Astrophysics Data System (ADS)

Delgado, Oihane; Campo-Bescós, Miguel A.; López, J. Javier

2017-04-01

Frequently, when we are trying to solve certain hydrological engineering problems, it is often necessary to know rain intensity values related to a specific probability or return period, T. Based on analyses of extreme rainfall events at different time scale aggregation, we can deduce the relationships among Intensity-Duration-Frequency (IDF), that are widely used in hydraulic infrastructure design. However, the lack of long time series of rainfall intensities for smaller time periods, minutes or hours, leads to use mathematical expressions to characterize and extend these curves. One way to deduce them is through the development of synthetic rainfall time series generated from stochastic models, which is evaluated in this work. From recorded accumulated rainfall time series every 10 min in the pluviograph of Igueldo (San Sebastian, Spain) for the time period between 1927-2005, their homogeneity has been checked and possible statistically significant increasing or decreasing trends have also been shown. Subsequently, two models have been calibrated: Bartlett-Lewis and Markov chains models, which are based on the successions of storms, composed for a series of rainfall events, separated by a short interval of time each. Finally, synthetic ten-minute rainfall time series are generated, which allow to estimate detailed IDF curves and compare them with the estimated IDF based on the recorded data.

Voltage and Current Clamp Transients with Membrane Dielectric Loss

PubMed Central

Fitzhugh, R.; Cole, K. S.

1973-01-01

Transient responses of a space-clamped squid axon membrane to step changes of voltage or current are often approximated by exponential functions of time, corresponding to a series resistance and a membrane capacity of 1.0 μF/cm2. Curtis and Cole (1938, J. Gen. Physiol. 21:757) found, however, that the membrane had a constant phase angle impedance z = z1(jωτ)-α, with a mean α = 0.85. (α = 1.0 for an ideal capacitor; α < 1.0 may represent dielectric loss.) This result is supported by more recently published experimental data. For comparison with experiments, we have computed functions expressing voltage and current transients with constant phase angle capacitance, a parallel leakage conductance, and a series resistance, at nine values of α from 0.5 to 1.0. A series in powers of tα provided a good approximation for short times; one in powers of t-α, for long times; for intermediate times, a rational approximation matching both series for a finite number of terms was used. These computations may help in determining experimental series resistances and parallel leakage conductances from membrane voltage or current clamp data. PMID:4754194

Inference of quantitative models of bacterial promoters from time-series reporter gene data.

PubMed

Stefan, Diana; Pinel, Corinne; Pinhal, Stéphane; Cinquemani, Eugenio; Geiselmann, Johannes; de Jong, Hidde

2015-01-01

The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations.

PubMed

Buras, Allan; van der Maaten-Theunissen, Marieke; van der Maaten, Ernst; Ahlgrimm, Svenja; Hermann, Philipp; Simard, Sonia; Heinrich, Ingo; Helle, Gerd; Unterseher, Martin; Schnittler, Martin; Eusemann, Pascal; Wilmking, Martin

2016-01-01

This paper introduces a new approach-the Principal Component Gradient Analysis (PCGA)-to detect ecological gradients in time-series populations, i.e. several time-series originating from different individuals of a population. Detection of ecological gradients is of particular importance when dealing with time-series from heterogeneous populations which express differing trends. PCGA makes use of polar coordinates of loadings from the first two axes obtained by principal component analysis (PCA) to define groups of similar trends. Based on the mean inter-series correlation (rbar) the gain of increasing a common underlying signal by PCGA groups is quantified using Monte Carlo Simulations. In terms of validation PCGA is compared to three other existing approaches. Focusing on dendrochronological examples, PCGA is shown to correctly determine population gradients and in particular cases to be advantageous over other considered methods. Furthermore, PCGA groups in each example allowed for enhancing the strength of a common underlying signal and comparably well as hierarchical cluster analysis. Our results indicate that PCGA potentially allows for a better understanding of mechanisms causing time-series population gradients as well as objectively enhancing the performance of climate transfer functions in dendroclimatology. While our examples highlight the relevance of PCGA to the field of dendrochronology, we believe that also other disciplines working with data of comparable structure may benefit from PCGA.

Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations

PubMed Central

Buras, Allan; van der Maaten-Theunissen, Marieke; van der Maaten, Ernst; Ahlgrimm, Svenja; Hermann, Philipp; Simard, Sonia; Heinrich, Ingo; Helle, Gerd; Unterseher, Martin; Schnittler, Martin; Eusemann, Pascal; Wilmking, Martin

2016-01-01

This paper introduces a new approach–the Principal Component Gradient Analysis (PCGA)–to detect ecological gradients in time-series populations, i.e. several time-series originating from different individuals of a population. Detection of ecological gradients is of particular importance when dealing with time-series from heterogeneous populations which express differing trends. PCGA makes use of polar coordinates of loadings from the first two axes obtained by principal component analysis (PCA) to define groups of similar trends. Based on the mean inter-series correlation (rbar) the gain of increasing a common underlying signal by PCGA groups is quantified using Monte Carlo Simulations. In terms of validation PCGA is compared to three other existing approaches. Focusing on dendrochronological examples, PCGA is shown to correctly determine population gradients and in particular cases to be advantageous over other considered methods. Furthermore, PCGA groups in each example allowed for enhancing the strength of a common underlying signal and comparably well as hierarchical cluster analysis. Our results indicate that PCGA potentially allows for a better understanding of mechanisms causing time-series population gradients as well as objectively enhancing the performance of climate transfer functions in dendroclimatology. While our examples highlight the relevance of PCGA to the field of dendrochronology, we believe that also other disciplines working with data of comparable structure may benefit from PCGA. PMID:27467508

High-resolution time series of Pseudomonas aeruginosa gene expression and rhamnolipid secretion through growth curve synchronization.

PubMed

van Ditmarsch, Dave; Xavier, João B

2011-06-17

Online spectrophotometric measurements allow monitoring dynamic biological processes with high-time resolution. Contrastingly, numerous other methods require laborious treatment of samples and can only be carried out offline. Integrating both types of measurement would allow analyzing biological processes more comprehensively. A typical example of this problem is acquiring quantitative data on rhamnolipid secretion by the opportunistic pathogen Pseudomonas aeruginosa. P. aeruginosa cell growth can be measured by optical density (OD600) and gene expression can be measured using reporter fusions with a fluorescent protein, allowing high time resolution monitoring. However, measuring the secreted rhamnolipid biosurfactants requires laborious sample processing, which makes this an offline measurement. Here, we propose a method to integrate growth curve data with endpoint measurements of secreted metabolites that is inspired by a model of exponential cell growth. If serial diluting an inoculum gives reproducible time series shifted in time, then time series of endpoint measurements can be reconstructed using calculated time shifts between dilutions. We illustrate the method using measured rhamnolipid secretion by P. aeruginosa as endpoint measurements and we integrate these measurements with high-resolution growth curves measured by OD600 and expression of rhamnolipid synthesis genes monitored using a reporter fusion. Two-fold serial dilution allowed integrating rhamnolipid measurements at a ~0.4 h-1 frequency with high-time resolved data measured at a 6 h-1 frequency. We show how this simple method can be used in combination with mutants lacking specific genes in the rhamnolipid synthesis or quorum sensing regulation to acquire rich dynamic data on P. aeruginosa virulence regulation. Additionally, the linear relation between the ratio of inocula and the time-shift between curves produces high-precision measurements of maximum specific growth rates, which were determined with a precision of ~5.4%. Growth curve synchronization allows integration of rich time-resolved data with endpoint measurements to produce time-resolved quantitative measurements. Such data can be valuable to unveil the dynamic regulation of virulence in P. aeruginosa. More generally, growth curve synchronization can be applied to many biological systems thus helping to overcome a key obstacle in dynamic regulation: the scarceness of quantitative time-resolved data.

LateBiclustering: Efficient Heuristic Algorithm for Time-Lagged Bicluster Identification.

PubMed

Gonçalves, Joana P; Madeira, Sara C

2014-01-01

Identifying patterns in temporal data is key to uncover meaningful relationships in diverse domains, from stock trading to social interactions. Also of great interest are clinical and biological applications, namely monitoring patient response to treatment or characterizing activity at the molecular level. In biology, researchers seek to gain insight into gene functions and dynamics of biological processes, as well as potential perturbations of these leading to disease, through the study of patterns emerging from gene expression time series. Clustering can group genes exhibiting similar expression profiles, but focuses on global patterns denoting rather broad, unspecific responses. Biclustering reveals local patterns, which more naturally capture the intricate collaboration between biological players, particularly under a temporal setting. Despite the general biclustering formulation being NP-hard, considering specific properties of time series has led to efficient solutions for the discovery of temporally aligned patterns. Notably, the identification of biclusters with time-lagged patterns, suggestive of transcriptional cascades, remains a challenge due to the combinatorial explosion of delayed occurrences. Herein, we propose LateBiclustering, a sensible heuristic algorithm enabling a polynomial rather than exponential time solution for the problem. We show that it identifies meaningful time-lagged biclusters relevant to the response of Saccharomyces cerevisiae to heat stress.

DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks

PubMed Central

Gerstein, Mark

2016-01-01

Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem’s gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally–e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org) for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the “state” and “control” in the model refer to its own (internal) and another subsystem’s (external) gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model’s parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation) representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs), seeing the degree to which these can be accounted for by orthologous (internal) versus species-specific (external) TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with evolutionarily ancient functions (e.g. the ribosomal proteins), in contrast to those with more recently evolved functions (e.g., cell-cell communication). This suggests that despite striking morphological differences, some fundamental embryonic-developmental processes are still controlled by ancient regulatory systems. PMID:27760135

DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks.

PubMed

Wang, Daifeng; He, Fei; Maslov, Sergei; Gerstein, Mark

2016-10-01

Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem's gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally-e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org) for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the "state" and "control" in the model refer to its own (internal) and another subsystem's (external) gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model's parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation) representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs), seeing the degree to which these can be accounted for by orthologous (internal) versus species-specific (external) TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with evolutionarily ancient functions (e.g. the ribosomal proteins), in contrast to those with more recently evolved functions (e.g., cell-cell communication). This suggests that despite striking morphological differences, some fundamental embryonic-developmental processes are still controlled by ancient regulatory systems.

Arbitrary-order corrections for finite-time drift and diffusion coefficients

NASA Astrophysics Data System (ADS)

Anteneodo, C.; Riera, R.

2009-09-01

We address a standard class of diffusion processes with linear drift and quadratic diffusion coefficients. These contributions to dynamic equations can be directly drawn from data time series. However, real data are constrained to finite sampling rates and therefore it is crucial to establish a suitable mathematical description of the required finite-time corrections. Based on Itô-Taylor expansions, we present the exact corrections to the finite-time drift and diffusion coefficients. These results allow to reconstruct the real hidden coefficients from the empirical estimates. We also derive higher-order finite-time expressions for the third and fourth conditional moments that furnish extra theoretical checks for this class of diffusion models. The analytical predictions are compared with the numerical outcomes of representative artificial time series.

Expression Templates for Truncated Power Series

NASA Astrophysics Data System (ADS)

Cary, John R.; Shasharina, Svetlana G.

1997-05-01

Truncated power series are used extensively in accelerator transport modeling for rapid tracking and analysis of nonlinearity. Such mathematical objects are naturally represented computationally as objects in C++. This is more intuitive and produces more transparent code through operator overloading. However, C++ object use often comes with a computational speed loss due, e.g., to the creation of temporaries. We have developed a subset of truncated power series expression templates(http://monet.uwaterloo.ca/blitz/). Such expression templates use the powerful template processing facility of C++ to combine complicated expressions into series operations that exectute more rapidly. We compare computational speeds with existing truncated power series libraries.

Automated Bayesian model development for frequency detection in biological time series.

PubMed

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.

Automated Bayesian model development for frequency detection in biological time series

PubMed Central

2011-01-01

Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure. PMID:21702910

Emergence of the self-similar property in gene expression dynamics

NASA Astrophysics Data System (ADS)

Ochiai, T.; Nacher, J. C.; Akutsu, T.

2007-08-01

Many theoretical models have recently been proposed to understand the structure of cellular systems composed of various types of elements (e.g., proteins, metabolites and genes) and their interactions. However, the cell is a highly dynamic system with thousands of functional elements fluctuating across temporal states. Therefore, structural analysis alone is not sufficient to reproduce the cell's observed behavior. In this article, we analyze the gene expression dynamics (i.e., how the amount of mRNA molecules in cell fluctuate in time) by using a new constructive approach, which reveals a symmetry embedded in gene expression fluctuations and characterizes the dynamical equation of gene expression (i.e., a specific stochastic differential equation). First, by using experimental data of human and yeast gene expression time series, we found a symmetry in short-time transition probability from time t to time t+1. We call it self-similarity symmetry (i.e., the gene expression short-time fluctuations contain a repeating pattern of smaller and smaller parts that are like the whole, but different in size). Secondly, we reconstruct the global behavior of the observed distribution of gene expression (i.e., scaling-law) and the local behavior of the power-law tail of this distribution. This approach may represent a step forward toward an integrated image of the basic elements of the whole cell.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates.

PubMed

Xia, Li C; Steele, Joshua A; Cram, Jacob A; Cardon, Zoe G; Simmons, Sheri L; Vallino, Joseph J; Fuhrman, Jed A; Sun, Fengzhu

2011-01-01

The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

PubMed Central

2011-01-01

Background The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. Results We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. Conclusions The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa. PMID:22784572

Inference of gene regulatory networks from time series by Tsallis entropy

PubMed Central

2011-01-01

Background The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 ≤ q ≤ 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/. PMID:21545720

Differential expression of the lethal gene Luteus-Pa in cacao of the Parinari series.

PubMed

Rehem, B C; Almeida, A-A F; Figueiredo, G S F; Gesteira, A S; Santos, S C; Corrêa, R X; Yamada, M M; Valle, R R

2016-02-22

The recessive lethal character Luteus-Pa is found in cacao (Theobroma cacao) genotypes of the Parinari series (Pa) and is characterized by expression of leaf chlorosis and seedling death. Several genotypes of the Pa series are bearers of the gene responsible for the expression of the Luteus-Pa character, which can be used as a tool for determining relationships between genotypes of this group. To evaluate this phenomenon, we analyzed the differential expression of genes between mutant seedlings and wild-type hybrid Pa 30 x 169 seedlings, with the aim of elucidating the possible lethal mechanisms of the homozygous recessive character Luteus-Pa. Plant material was harvested from leaves of wild and mutant seedlings at different periods to construct a subtractive library and perform quantitative analysis using real-time PCR. The 649 sequences obtained from the subtractive library had an average length of 500 bp, forming 409 contigs. The probable proteins encoded were grouped into 10 functional categories. Data from ESTs identified genes associated with Rubisco, peroxidases, and other proteins and enzymes related to carbon assimilation, respiration, and photosystem 2. Mutant seedlings were characterized by synthesizing defective PsbO and PsbA proteins, which were overexpressed from 15 to 20 days after seedling emergence.

Time-Series Transcriptomics Reveals That AGAMOUS-LIKE22 Affects Primary Metabolism and Developmental Processes in Drought-Stressed Arabidopsis[OPEN

PubMed Central

Penfold, Christopher A.; Jenkins, Dafyd J.; Legaie, Roxane; Lawson, Tracy; Vialet-Chabrand, Silvere R.M.; Subramaniam, Sunitha; Hickman, Richard; Feil, Regina; Bowden, Laura; Hill, Claire; Lunn, John E.; Finkenstädt, Bärbel; Buchanan-Wollaston, Vicky; Beynon, Jim; Wild, David L.; Ott, Sascha

2016-01-01

In Arabidopsis thaliana, changes in metabolism and gene expression drive increased drought tolerance and initiate diverse drought avoidance and escape responses. To address regulatory processes that link these responses, we set out to identify genes that govern early responses to drought. To do this, a high-resolution time series transcriptomics data set was produced, coupled with detailed physiological and metabolic analyses of plants subjected to a slow transition from well-watered to drought conditions. A total of 1815 drought-responsive differentially expressed genes were identified. The early changes in gene expression coincided with a drop in carbon assimilation, and only in the late stages with an increase in foliar abscisic acid content. To identify gene regulatory networks (GRNs) mediating the transition between the early and late stages of drought, we used Bayesian network modeling of differentially expressed transcription factor (TF) genes. This approach identified AGAMOUS-LIKE22 (AGL22), as key hub gene in a TF GRN. It has previously been shown that AGL22 is involved in the transition from vegetative state to flowering but here we show that AGL22 expression influences steady state photosynthetic rates and lifetime water use. This suggests that AGL22 uniquely regulates a transcriptional network during drought stress, linking changes in primary metabolism and the initiation of stress responses. PMID:26842464

Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment.

PubMed

Yang, Yi; Maxwell, Andrew; Zhang, Xiaowei; Wang, Nan; Perkins, Edward J; Zhang, Chaoyang; Gong, Ping

2013-01-01

Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting. Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways. Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach. Findings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.

[Expression of negative emotional responses to the 2011 Great East Japan Earthquake: Analysis of big data from social media].

PubMed

Miura, Asako; Komori, Masashi; Matsumura, Naohiro; Maeda, Kazutoshi

2015-06-01

In this article, we investigated the expression of emotional responses to the 2011 Great East Japan Earthquake by analyzing the frequency of negative emotional terms in tweets posted on Twitter, one of the most popular social media platforms. We focused on differences in time-series variations and diurnal changes between two kinds of disasters: natural disasters (earthquakes and tsunamis) and nuclear accidents. The number of tweets containing negative emotional responses increased sharply shortly after the first huge earthquake and decreased over time, whereas tweets about nuclear accidents showed no correlation with elapsed time. Expressions of anxiety about natural disasters had a circadian rhythm, with a peak at midnight, whereas expressions of anger about the nuclear accident were highly sensitive to critical events related to the accident. These findings were discussed in terms of similarities and differences compared to earlier studies on emotional responses in social media.

Spectral Analysis: From Additive Perspective to Multiplicative Perspective

NASA Astrophysics Data System (ADS)

Wu, Z.

2017-12-01

The early usage of trigonometric functions can be traced back to at least 17th century BC. It was Bhaskara II of the 12th century CE who first proved the mathematical equivalence between the sum of two trigonometric functions of any given angles and the product of two trigonometric functions of related angles, which has been taught these days in middle school classroom. The additive perspective of trigonometric functions led to the development of the Fourier transform that is used to express any functions as the sum of a set of trigonometric functions and opened a new mathematical field called harmonic analysis. Unfortunately, Fourier's sum cannot directly express nonlinear interactions between trigonometric components of different periods, and thereby lacking the capability of quantifying nonlinear interactions in dynamical systems. In this talk, the speaker will introduce the Huang transform and Holo-spectrum which were pioneered by Norden Huang and emphasizes the multiplicative perspective of trigonometric functions in expressing any function. Holo-spectrum is a multi-dimensional spectral expression of a time series that explicitly identifies the interactions among different scales and quantifies nonlinear interactions hidden in a time series. Along with this introduction, the developing concepts of physical, rather than mathematical, analysis of data will be explained. Various enlightening applications of Holo-spectrum analysis in atmospheric and climate studies will also be presented.

Detecting and interpreting distortions in hierarchical organization of complex time series

NASA Astrophysics Data System (ADS)

DroŻdŻ, Stanisław; OświÈ©cimka, Paweł

2015-03-01

Hierarchical organization is a cornerstone of complexity and multifractality constitutes its central quantifying concept. For model uniform cascades the corresponding singularity spectra are symmetric while those extracted from empirical data are often asymmetric. Using selected time series representing such diverse phenomena as price changes and intertransaction times in financial markets, sentence length variability in narrative texts, Missouri River discharge, and sunspot number variability as examples, we show that the resulting singularity spectra appear strongly asymmetric, more often left sided but in some cases also right sided. We present a unified view on the origin of such effects and indicate that they may be crucially informative for identifying the composition of the time series. One particularly intriguing case of this latter kind of asymmetry is detected in the daily reported sunspot number variability. This signals that either the commonly used famous Wolf formula distorts the real dynamics in expressing the largest sunspot numbers or, if not, that their dynamics is governed by a somewhat different mechanism.

Waning of "conditioned pain modulation": a novel expression of subtle pronociception in migraine.

PubMed

Nahman-Averbuch, Hadas; Granovsky, Yelena; Coghill, Robert C; Yarnitsky, David; Sprecher, Elliot; Weissman-Fogel, Irit

2013-01-01

To assess the decay of the conditioned pain modulation (CPM) response along repeated applications as a possible expression of subtle pronociception in migraine. One of the most explored mechanisms underlying the pain modulation system is "diffuse noxious inhibitory controls," which is measured psychophysically in the lab by the CPM paradigm. There are contradicting reports on CPM response in migraine, questioning whether migraineurs express pronociceptive pain modulation. Migraineurs (n = 26) and healthy controls (n = 35), all females, underwent 3 stimulation series, consisting of repeated (1) "test-stimulus" (Ts) alone that was given first followed by (2) parallel CPM application (CPM-parallel), and (3) sequential CPM application (CPM-sequential), in which the Ts is delivered during or following the conditioning-stimulus, respectively. In all series, the Ts repeated 4 times (0-3). In the CPM series, repetition "0" consisted of the Ts-alone that was followed by 3 repetitions of the Ts with a conditioning-stimulus application. Although there was no difference between migraineurs and controls for the first CPM response in each series, we found waning of CPM-parallel efficiency along the series for migraineurs (P = .005 for third vs first CPM), but not for controls. Further, greater CPM waning in the CPM-sequential series was correlated with less reported extent of pain reduction by episodic medication (r = 0.493, P = .028). Migraineurs have subtle deficits in endogenous pain modulation which requires a more challenging test protocol than the commonly used single CPM. Waning of CPM response seems to reveal this pronociceptive state. The clinical relevance of the CPM waning effect is highlighted by its association with clinical parameters of migraine. © 2013 American Headache Society.

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.

PubMed

Gordonov, Simon; Hwang, Mun Kyung; Wells, Alan; Gertler, Frank B; Lauffenburger, Douglas A; Bathe, Mark

2016-01-01

Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.

Analysis on Difference of Forest Phenology Extracted from EVI and LAI Based on PhenoCams

NASA Astrophysics Data System (ADS)

Wang, C.; Jing, L.; Qinhuo, L.

2017-12-01

Land surface phenology can make up for the deficiency of field observation with advantages of capturing the continuous expression of phenology on a large scale. However, there are some variability in phenological metrics derived from different satellite time-series data of vegetation parameters. This paper aims at assessing the difference of phenology information extracted from EVI and LAI time series. To achieve this, some web-camera sites were selected to analyze the characteristics between MODIS-EVI and MODIS-LAI time series from 2010 to 2014 for different forest types, including evergreen coniferous forest, evergreen broadleaf forest, deciduous coniferous forest and deciduous broadleaf forest. At the same time, satellite-based phenological metrics were extracted by the Logistics algorithm and compared with camera-based phenological metrics. Results show that the SOS and EOS that are extracted from LAI are close to bud burst and leaf defoliation respectively, while the SOS and EOS that are extracted from EVI is close to leaf unfolding and leaf coloring respectively. Thus the SOS that is extracted from LAI is earlier than that from EVI, while the EOS that is extracted from LAI is later than that from EVI at deciduous forest sites. Although the seasonal variation characteristics of evergreen forests are not apparent, significant discrepancies exist in LAI time series and EVI time series. In addition, Satellite- and camera-based phenological metrics agree well generally, but EVI has higher correlation with the camera-based canopy greenness (green chromatic coordinate, gcc) than LAI.

SiGN-SSM: open source parallel software for estimating gene networks with state space models.

PubMed

Tamada, Yoshinori; Yamaguchi, Rui; Imoto, Seiya; Hirose, Osamu; Yoshida, Ryo; Nagasaki, Masao; Miyano, Satoru

2011-04-15

SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles. SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site. tamada@ims.u-tokyo.ac.jp.

The effect of transverse wave vector and magnetic fields on resonant tunneling times in double-barrier structures

NASA Astrophysics Data System (ADS)

Wang, Hongmei; Zhang, Yafei; Xu, Huaizhe

2007-01-01

The effect of transverse wave vector and magnetic fields on resonant tunneling times in double-barrier structures, which is significant but has been frequently omitted in previous theoretical methods, has been reported in this paper. The analytical expressions of the longitudinal energies of quasibound levels (LEQBL) and the lifetimes of quasibound levels (LQBL) in symmetrical double-barrier (SDB) structures have been derived as a function of transverse wave vector and longitudinal magnetic fields perpendicular to interfaces. Based on our derived analytical expressions, the LEQBL and LQBL dependence upon transverse wave vector and longitudinal magnetic fields has been explored numerically for a SDB structure. Model calculations show that the LEQBL decrease monotonically and the LQBL shorten with increasing transverse wave vector, and each original LEQBL splits to a series of sub-LEQBL which shift nearly linearly toward the well bottom and the lifetimes of quasibound level series (LQBLS) shorten with increasing Landau-level indices and magnetic fields.

Differentially expressed genes of Tetrahymena thermophila in response to tributyltin (TBT) identified by suppression subtractive hybridization and real time quantitative PCR.

PubMed

Feng, Lifang; Miao, Wei; Wu, Yuxuan

2007-02-15

Tributyltin (TBT) is widely used as antifouling paints, agriculture biocides, and plastic stabilizers around the world, resulting in great pollution problem in aquatic environments. However, it has been short of the biomonitor to detect TBT in freshwater. We constructed the suppression subtractive hybridization library of Tetrahymena thermophila exposed to TBT, and screened out 101 Expressed Sequence Tags whose expressions were significantly up- or down-regulated with TBT treatment. From this, a series of genes related to the TBT toxicity were discovered, such as glutathione-S-transferase gene (down-regulated), plasma membrane Ca2+ ATPase isoforms 3 gene (up-regulated) and NgoA (up-regulated). Furthermore, their expressions under different concentrations of TBT treatment (0.5-40 ppb) were detected by real time fluorescent quantitative PCR. The differentially expressed genes of T. thermophila in response to TBT were identified, which provide the basic to make Tetrahymena as a sensitive, rapid and convenient TBT biomonitor in freshwater based on rDNA inducible expression system.

Unraveling multiple changes in complex climate time series using Bayesian inference

NASA Astrophysics Data System (ADS)

Berner, Nadine; Trauth, Martin H.; Holschneider, Matthias

2016-04-01

Change points in time series are perceived as heterogeneities in the statistical or dynamical characteristics of observations. Unraveling such transitions yields essential information for the understanding of the observed system. The precise detection and basic characterization of underlying changes is therefore of particular importance in environmental sciences. We present a kernel-based Bayesian inference approach to investigate direct as well as indirect climate observations for multiple generic transition events. In order to develop a diagnostic approach designed to capture a variety of natural processes, the basic statistical features of central tendency and dispersion are used to locally approximate a complex time series by a generic transition model. A Bayesian inversion approach is developed to robustly infer on the location and the generic patterns of such a transition. To systematically investigate time series for multiple changes occurring at different temporal scales, the Bayesian inversion is extended to a kernel-based inference approach. By introducing basic kernel measures, the kernel inference results are composed into a proxy probability to a posterior distribution of multiple transitions. Thus, based on a generic transition model a probability expression is derived that is capable to indicate multiple changes within a complex time series. We discuss the method's performance by investigating direct and indirect climate observations. The approach is applied to environmental time series (about 100 a), from the weather station in Tuscaloosa, Alabama, and confirms documented instrumentation changes. Moreover, the approach is used to investigate a set of complex terrigenous dust records from the ODP sites 659, 721/722 and 967 interpreted as climate indicators of the African region of the Plio-Pleistocene period (about 5 Ma). The detailed inference unravels multiple transitions underlying the indirect climate observations coinciding with established global climate events.

Structural Equation Modeling of Multivariate Time Series

ERIC Educational Resources Information Center

du Toit, Stephen H. C.; Browne, Michael W.

2007-01-01

The covariance structure of a vector autoregressive process with moving average residuals (VARMA) is derived. It differs from other available expressions for the covariance function of a stationary VARMA process and is compatible with current structural equation methodology. Structural equation modeling programs, such as LISREL, may therefore be…

Statistical indicators of collective behavior and functional clusters in gene networks of yeast

NASA Astrophysics Data System (ADS)

Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

Kumaraswamy autoregressive moving average models for double bounded environmental data

NASA Astrophysics Data System (ADS)

Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme

2017-12-01

In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects

PubMed Central

2012-01-01

Background Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with autoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models. Results We illustrate the applicability of our new model using synthetic and real time-course datasets. We show that our model outperforms existing models to provide more reliable and robust clustering of time-course data. Our model provides superior results when genetic profiles are correlated. It also gives comparable results when the correlation between the gene profiles is weak. In the applications to real time-course data, relevant clusters of coregulated genes are obtained, which are supported by gene-function annotation databases. Conclusions Our new model under our extension of the EMMIX-WIRE procedure is more reliable and robust for clustering time-course data because it adopts a random effects model that allows for the correlation among observations at different time points. It postulates gene-specific random effects with an autocorrelation variance structure that models coregulation within the clusters. The developed R package is flexible in its specification of the random effects through user-input parameters that enables improved modelling and consequent clustering of time-course data. PMID:23151154

A time-series method for automated measurement of changes in mitotic and interphase duration from time-lapse movies.

PubMed

Sigoillot, Frederic D; Huckins, Jeremy F; Li, Fuhai; Zhou, Xiaobo; Wong, Stephen T C; King, Randall W

2011-01-01

Automated time-lapse microscopy can visualize proliferation of large numbers of individual cells, enabling accurate measurement of the frequency of cell division and the duration of interphase and mitosis. However, extraction of quantitative information by manual inspection of time-lapse movies is too time-consuming to be useful for analysis of large experiments. Here we present an automated time-series approach that can measure changes in the duration of mitosis and interphase in individual cells expressing fluorescent histone 2B. The approach requires analysis of only 2 features, nuclear area and average intensity. Compared to supervised learning approaches, this method reduces processing time and does not require generation of training data sets. We demonstrate that this method is as sensitive as manual analysis in identifying small changes in interphase or mitotic duration induced by drug or siRNA treatment. This approach should facilitate automated analysis of high-throughput time-lapse data sets to identify small molecules or gene products that influence timing of cell division.

Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

PubMed

Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

2014-01-01

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

Wavelet application to the time series analysis of DORIS station coordinates

NASA Astrophysics Data System (ADS)

Bessissi, Zahia; Terbeche, Mekki; Ghezali, Boualem

2009-06-01

The topic developed in this article relates to the residual time series analysis of DORIS station coordinates using the wavelet transform. Several analysis techniques, already developed in other disciplines, were employed in the statistical study of the geodetic time series of stations. The wavelet transform allows one, on the one hand, to provide temporal and frequential parameter residual signals, and on the other hand, to determine and quantify systematic signals such as periodicity and tendency. Tendency is the change in short or long term signals; it is an average curve which represents the general pace of the signal evolution. On the other hand, periodicity is a process which is repeated, identical to itself, after a time interval called the period. In this context, the topic of this article consists, on the one hand, in determining the systematic signals by wavelet analysis of time series of DORIS station coordinates, and on the other hand, in applying the denoising signal to the wavelet packet, which makes it possible to obtain a well-filtered signal, smoother than the original signal. The DORIS data used in the treatment are a set of weekly residual time series from 1993 to 2004 from eight stations: DIOA, COLA, FAIB, KRAB, SAKA, SODB, THUB and SYPB. It is the ign03wd01 solution expressed in stcd format, which is derived by the IGN/JPL analysis center. Although these data are not very recent, the goal of this study is to detect the contribution of the wavelet analysis method on the DORIS data, compared to the other analysis methods already studied.

Electron-temperature dependence of dissociative recombination of electrons with CO/+/./CO/n-series ions

NASA Technical Reports Server (NTRS)

Whitaker, M.; Biondi, M. A.; Johnsen, R.

1981-01-01

The dependence on electron temperature of the coefficients for electron recombination with molecular cluster ions of the carbon monoxide series, CO(+).(CO)n, is determined. A microwave discharge lasting approximately 0.1 msec was applied in 5-20 Torr neon containing a few tenths percent CO in an afterglow mass spectrometer apparatus, and the time histories of the various afterglow ions were measured. Expressions for the dependence of the recombination coefficients of the dimer and trimer ions CO(+).CO and CO(+).(CO)2 are obtained which are found to be significantly different from those previously obtained for hydronium and ammonium series polar cluster ions, but similar to those of simple diatomic ions.

Hilbert-Schmidt and Sobol sensitivity indices for static and time series Wnt signaling measurements in colorectal cancer - part A.

PubMed

Sinha, Shriprakash

2017-12-04

Ever since the accidental discovery of Wingless [Sharma R.P., Drosophila information service, 1973, 50, p 134], research in the field of Wnt signaling pathway has taken significant strides in wet lab experiments and various cancer clinical trials, augmented by recent developments in advanced computational modeling of the pathway. Information rich gene expression profiles reveal various aspects of the signaling pathway and help in studying different issues simultaneously. Hitherto, not many computational studies exist which incorporate the simultaneous study of these issues. This manuscript ∙ explores the strength of contributing factors in the signaling pathway, ∙ analyzes the existing causal relations among the inter/extracellular factors effecting the pathway based on prior biological knowledge and ∙ investigates the deviations in fold changes in the recently found prevalence of psychophysical laws working in the pathway. To achieve this goal, local and global sensitivity analysis is conducted on the (non)linear responses between the factors obtained from static and time series expression profiles using the density (Hilbert-Schmidt Information Criterion) and variance (Sobol) based sensitivity indices. The results show the advantage of using density based indices over variance based indices mainly due to the former's employment of distance measures & the kernel trick via Reproducing kernel Hilbert space (RKHS) that capture nonlinear relations among various intra/extracellular factors of the pathway in a higher dimensional space. In time series data, using these indices it is now possible to observe where in time, which factors get influenced & contribute to the pathway, as changes in concentration of the other factors are made. This synergy of prior biological knowledge, sensitivity analysis & representations in higher dimensional spaces can facilitate in time based administration of target therapeutic drugs & reveal hidden biological information within colorectal cancer samples.

Electromagnetic pulse propagation in dispersive planar dielectrics.

PubMed

Moten, K; Durney, C H; Stockham, T G

1989-01-01

The responses of a plane-wave pulse train irradiating a lossy dispersive dielectric half-space are investigated. The incident pulse train is expressed as a Fourier series with summing done by the inverse fast Fourier transform. The Fourier series technique is adopted to avoid the many difficulties often encountered in finding the inverse Fourier transform when transform analyses are used. Calculations are made for propagation in pure water, and typical waveforms inside the dielectric half-space are presented. Higher harmonics are strongly attenuated, resulting in a single continuous sinusoidal waveform at the frequency of the fundamental depth in the material. The time-averaged specific absorption rate (SAR) for pulse-train propagation is shown to be the sum of the time-averaged SARs of the individual harmonic components of the pulse train. For the same average power, calculated SARs reveal that pulse trains generally penetrate deeper than carrier-frequency continuous waves but not deeper than continuous waves at frequencies approaching the fundamental of the pulse train. The effects of rise time on the propagating pulse train in the dielectrics are shown and explained. Since most practical pulsed systems are very limited in bandwidth, no pronounced differences between their response and continuous wave (CW) response would be expected. Typical results for pulse-train propagation in arrays of dispersive planar dielectric slabs are presented. Expressing the pulse train as a Fourier series provides a practical way of interpreting the dispersion characteristics from the spectral point of view.

Analysis of time series for postal shipments in Regional VII East Java Indonesia

NASA Astrophysics Data System (ADS)

Kusrini, DE; Ulama, B. S. S.; Aridinanti, L.

2018-03-01

The change of number delivery goods through PT. Pos Regional VII East Java Indonesia indicates that the trend of increasing and decreasing the delivery of documents and non-documents in PT. Pos Regional VII East Java Indonesia is strongly influenced by conditions outside of PT. Pos Regional VII East Java Indonesia so that the prediction the number of document and non-documents requires a model that can accommodate it. Based on the time series plot monthly data fluctuations occur from 2013-2016 then the model is done using ARIMA or seasonal ARIMA and selected the best model based on the smallest AIC value. The results of data analysis about the number of shipments on each product sent through the Sub-Regional Postal Office VII East Java indicates that there are 5 post offices of 26 post offices entering the territory. The largest number of shipments is available on the PPB (Paket Pos Biasa is regular package shipment/non-document ) and SKH (Surat Kilat Khusus is Special Express Mail/document) products. The time series model generated is largely a Random walk model meaning that the number of shipment in the future is influenced by random effects that are difficult to predict. Some are AR and MA models, except for Express shipment products with Malang post office destination which has seasonal ARIMA model on lag 6 and 12. This means that the number of items in the following month is affected by the number of items in the previous 6 months.

CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis.

PubMed

Vrahatis, Aristidis G; Dimitrakopoulou, Konstantina; Balomenos, Panos; Tsakalidis, Athanasios K; Bezerianos, Anastasios

2016-03-15

In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific 'active parts' of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical 'themes'-in the form of enriched biologically relevant microRNA-mediated subpathways-that determine the functionality of signaling networks across time. To address these challenges, we developed time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) by integrating time series mRNA/microRNA expression data with KEGG pathway maps and microRNA-target interactions. Specifically, microRNA-mediated subpathway topologies are extracted and evaluated based on the temporal transition and the fold change activity of the linked genes/microRNAs. Further, we provide measures that capture the structural and functional features of subpathways in relation to the complete organism pathway atlas. Our application to synthetic and real data shows that CHRONOS outperforms current subpathway-based methods into unraveling the inherent dynamic properties of pathways. CHRONOS is freely available at http://biosignal.med.upatras.gr/chronos/ tassos.bezerianos@nus.edu.sg Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Eye-Specific Gene Expression following Embryonic Ethanol Exposure in Zebrafish: Roles for Heat Shock Factor 1

PubMed Central

Kashyap, Bhavani; Pegorsch, Laurel; Frey, Ruth A.; Sun, Chi; Shelden, Eric A.; Stenkamp, Deborah L.

2014-01-01

The mechanisms through which ethanol exposure results in developmental defects remain unclear. We used the zebrafish model to elucidate eye-specific mechanisms that underlie ethanol-mediated microphthalmia (reduced eye size), through time-series microarray analysis of gene expression within eyes of embryos exposed to 1.5% ethanol. 62 genes were differentially expressed (DE) in ethanol-treated as compared to control eyes sampled during retinal neurogenesis (24-48 hours post-fertilization). The EDGE (extraction of differential gene expression) algorithm identified >3000 genes DE over developmental time in ethanol-exposed eyes as compared to controls. The DE lists included several genes indicating a mis-regulated cellular stress response due to ethanol exposure. Combined treatment with sub-threshold levels of ethanol and a morpholino targeting heat shock factor 1 mRNA resulted in microphthalmia, suggesting convergent molecular pathways. Thermal preconditioning partially prevented ethanol-mediated microphthalmia while maintaining Hsf-1 expression. These data suggest roles for reduced Hsf-1 in mediating microphthalmic effects of embryonic ethanol exposure. PMID:24355176

Linking melodic expectation to expressive performance timing and perceived musical tension.

PubMed

Gingras, Bruno; Pearce, Marcus T; Goodchild, Meghan; Dean, Roger T; Wiggins, Geraint; McAdams, Stephen

2016-04-01

This research explored the relations between the predictability of musical structure, expressive timing in performance, and listeners' perceived musical tension. Studies analyzing the influence of expressive timing on listeners' affective responses have been constrained by the fact that, in most pieces, the notated durations limit performers' interpretive freedom. To circumvent this issue, we focused on the unmeasured prelude, a semi-improvisatory genre without notated durations. In Experiment 1, 12 professional harpsichordists recorded an unmeasured prelude on a harpsichord equipped with a MIDI console. Melodic expectation was assessed using a probabilistic model (IDyOM [Information Dynamics of Music]) whose expectations have been previously shown to match closely those of human listeners. Performance timing information was extracted from the MIDI data using a score-performance matching algorithm. Time-series analyses showed that, in a piece with unspecified note durations, the predictability of melodic structure measurably influenced tempo fluctuations in performance. In Experiment 2, another 10 harpsichordists, 20 nonharpsichordist musicians, and 20 nonmusicians listened to the recordings from Experiment 1 and rated the perceived tension continuously. Granger causality analyses were conducted to investigate predictive relations among melodic expectation, expressive timing, and perceived tension. Although melodic expectation, as modeled by IDyOM, modestly predicted perceived tension for all participant groups, neither of its components, information content or entropy, was Granger causal. In contrast, expressive timing was a strong predictor and was Granger causal. However, because melodic expectation was also predictive of expressive timing, our results outline a complete chain of influence from predictability of melodic structure via expressive performance timing to perceived musical tension. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Long non-coding RNAs and mRNAs profiling during spleen development in pig.

PubMed

Che, Tiandong; Li, Diyan; Jin, Long; Fu, Yuhua; Liu, Yingkai; Liu, Pengliang; Wang, Yixin; Tang, Qianzi; Ma, Jideng; Wang, Xun; Jiang, Anan; Li, Xuewei; Li, Mingzhou

2018-01-01

Genome-wide transcriptomic studies in humans and mice have become extensive and mature. However, a comprehensive and systematic understanding of protein-coding genes and long non-coding RNAs (lncRNAs) expressed during pig spleen development has not been achieved. LncRNAs are known to participate in regulatory networks for an array of biological processes. Here, we constructed 18 RNA libraries from developing fetal pig spleen (55 days before birth), postnatal pig spleens (0, 30, 180 days and 2 years after birth), and the samples from the 2-year-old Wild Boar. A total of 15,040 lncRNA transcripts were identified among these samples. We found that the temporal expression pattern of lncRNAs was more restricted than observed for protein-coding genes. Time-series analysis showed two large modules for protein-coding genes and lncRNAs. The up-regulated module was enriched for genes related to immune and inflammatory function, while the down-regulated module was enriched for cell proliferation processes such as cell division and DNA replication. Co-expression networks indicated the functional relatedness between protein-coding genes and lncRNAs, which were enriched for similar functions over the series of time points examined. We identified numerous differentially expressed protein-coding genes and lncRNAs in all five developmental stages. Notably, ceruloplasmin precursor (CP), a protein-coding gene participating in antioxidant and iron transport processes, was differentially expressed in all stages. This study provides the first catalog of the developing pig spleen, and contributes to a fuller understanding of the molecular mechanisms underpinning mammalian spleen development.

GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data.

PubMed

Rue-Albrecht, Kévin; McGettigan, Paul A; Hernández, Belinda; Nalpas, Nicolas C; Magee, David A; Parnell, Andrew C; Gordon, Stephen V; MacHugh, David E

2016-03-11

Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.

Standard deviation of the mean and other time series properties of voltages measured with a digital lock-in amplifier

NASA Astrophysics Data System (ADS)

Witt, Thomas J.; Fletcher, N. E.

2010-10-01

We investigate some statistical properties of ac voltages from a white noise source measured with a digital lock-in amplifier equipped with finite impulse response output filters which introduce correlations between successive voltage values. The main goal of this work is to propose simple solutions to account for correlations when calculating the standard deviation of the mean (SDM) for a sequence of measurement data acquired using such an instrument. The problem is treated by time series analysis based on a moving average model of the filtering process. Theoretical expressions are derived for the power spectral density (PSD), the autocorrelation function, the equivalent noise bandwidth and the Allan variance; all are related to the SDM. At most three parameters suffice to specify any of the above quantities: the filter time constant, the time between successive measurements (both set by the lock-in operator) and the PSD of the white noise input, h0. Our white noise source is a resistor so that the PSD is easily calculated; there are no free parameters. Theoretical expressions are checked against their respective sample estimates and, with the exception of two of the bandwidth estimates, agreement to within 11% or better is found.

Transcriptional noise in intact and TGF-beta treated human kidney cells; the importance of time-series designs.

PubMed

Rabieian, Reyhaneh; Moein, Shiva; Khanahmad, Hossein; Mortazavi, Mojgan; Gheisari, Yousof

2018-05-26

The transforming growth factor (TGF)-β signaling pathway plays a key role in various cellular processes. However, insufficient knowledge about the complex and sometimes paradoxical functions of this pathway hinders its therapeutic targeting. In this study, the transcriptional profile of seven mediators and downstream elements of the TGF-β pathway were assessed in TGF-β treated and untreated human kidney derived cells for 2 weeks in a time course manner. As expected the up-regulation of ACTA2 and COL1A2 was evident in the treated cells. However, we observed remarkable fluctuations in gene expression, even in the supposedly steady states. The magnitude of noise was diverse in the examined genes. Our findings underscore the significance of time-course designs for gene expression analyses and clearly show that misleading data can be obtained in single point measurements. Furthermore, we propose specific considerations in the interpretation of time-course data in the context of noisy gene expression. © 2018 International Federation for Cell Biology.

Selecting the most appropriate time points to profile in high-throughput studies

PubMed Central

Kleyman, Michael; Sefer, Emre; Nicola, Teodora; Espinoza, Celia; Chhabra, Divya; Hagood, James S; Kaminski, Naftali; Ambalavanan, Namasivayam; Bar-Joseph, Ziv

2017-01-01

Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection (TPS) method that solves this combinatorial problem in a principled and practical way. TPS utilizes expression data from a small set of genes sampled at a high rate. As we show by applying TPS to study mouse lung development, the points selected by TPS can be used to reconstruct an accurate representation for the expression values of the non selected points. Further, even though the selection is only based on gene expression, these points are also appropriate for representing a much larger set of protein, miRNA and DNA methylation changes over time. TPS can thus serve as a key design strategy for high throughput time series experiments. Supporting Website: www.sb.cs.cmu.edu/TPS DOI: http://dx.doi.org/10.7554/eLife.18541.001 PMID:28124972

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

PubMed Central

Hallac, David; Vare, Sagar; Boyd, Stephen; Leskovec, Jure

2018-01-01

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios. PMID:29770257

Non-Markovian properties and multiscale hidden Markovian network buried in single molecule time series

NASA Astrophysics Data System (ADS)

Sultana, Tahmina; Takagi, Hiroaki; Morimatsu, Miki; Teramoto, Hiroshi; Li, Chun-Biu; Sako, Yasushi; Komatsuzaki, Tamiki

2013-12-01

We present a novel scheme to extract a multiscale state space network (SSN) from single-molecule time series. The multiscale SSN is a type of hidden Markov model that takes into account both multiple states buried in the measurement and memory effects in the process of the observable whenever they exist. Most biological systems function in a nonstationary manner across multiple timescales. Combined with a recently established nonlinear time series analysis based on information theory, a simple scheme is proposed to deal with the properties of multiscale and nonstationarity for a discrete time series. We derived an explicit analytical expression of the autocorrelation function in terms of the SSN. To demonstrate the potential of our scheme, we investigated single-molecule time series of dissociation and association kinetics between epidermal growth factor receptor (EGFR) on the plasma membrane and its adaptor protein Ash/Grb2 (Grb2) in an in vitro reconstituted system. We found that our formula successfully reproduces their autocorrelation function for a wide range of timescales (up to 3 s), and the underlying SSNs change their topographical structure as a function of the timescale; while the corresponding SSN is simple at the short timescale (0.033-0.1 s), the SSN at the longer timescales (0.1 s to ˜3 s) becomes rather complex in order to capture multiscale nonstationary kinetics emerging at longer timescales. It is also found that visiting the unbound form of the EGFR-Grb2 system approximately resets all information of history or memory of the process.

Approximate Entropies for Stochastic Time Series and EKG Time Series of Patients with Epilepsy and Pseudoseizures

NASA Astrophysics Data System (ADS)

Vyhnalek, Brian; Zurcher, Ulrich; O'Dwyer, Rebecca; Kaufman, Miron

2009-10-01

A wide range of heart rate irregularities have been reported in small studies of patients with temporal lobe epilepsy [TLE]. We hypothesize that patients with TLE display cardiac dysautonomia in either a subclinical or clinical manner. In a small study, we have retrospectively identified (2003-8) two groups of patients from the epilepsy monitoring unit [EMU] at the Cleveland Clinic. No patients were diagnosed with cardiovascular morbidities. The control group consisted of patients with confirmed pseudoseizures and the experimental group had confirmed right temporal lobe epilepsy through a seizure free outcome after temporal lobectomy. We quantified the heart rate variability using the approximate entropy [ApEn]. We found similar values of the ApEn in all three states of consciousness (awake, sleep, and proceeding seizure onset). In the TLE group, there is some evidence for greater variability in the awake than in either the sleep or proceeding seizure onset. Here we present results for mathematically-generated time series: the heart rate fluctuations ξ follow the γ statistics i.e., p(ξ)=γ-1(k) ξ^k exp(-ξ). This probability function has well-known properties and its Shannon entropy can be expressed in terms of the γ-function. The parameter k allows us to generate a family of heart rate time series with different statistics. The ApEn calculated for the generated time series for different values of k mimic the properties found for the TLE and pseudoseizure group. Our results suggest that the ApEn is an effective tool to probe differences in statistics of heart rate fluctuations.

Advanced interferometric synthetic aperture radar (InSAR) time series analysis using interferograms of multiple-orbit tracks: A case study on Miyake-jima

NASA Astrophysics Data System (ADS)

Ozawa, Taku; Ueda, Hideki

2011-12-01

InSAR time series analysis is an effective tool for detecting spatially and temporally complicated volcanic deformation. To obtain details of such deformation, we developed an advanced InSAR time series analysis using interferograms of multiple-orbit tracks. Considering only right- (or only left-) looking SAR observations, incidence directions for different orbit tracks are mostly included in a common plane. Therefore, slant-range changes in their interferograms can be expressed by two components in the plane. This approach estimates the time series of their components from interferograms of multiple-orbit tracks by the least squares analysis, and higher accuracy is obtained if many interferograms of different orbit tracks are available. Additionally, this analysis can combine interferograms for different incidence angles. In a case study on Miyake-jima, we obtained a deformation time series corresponding to GPS observations from PALSAR interferograms of six orbit tracks. The obtained accuracy was better than that with the SBAS approach, demonstrating its effectiveness. Furthermore, it is expected that higher accuracy would be obtained if SAR observations were carried out more frequently in all orbit tracks. The deformation obtained in the case study indicates uplift along the west coast and subsidence with contraction around the caldera. The speed of the uplift was almost constant, but the subsidence around the caldera decelerated from 2009. A flat deformation source was estimated near sea level under the caldera, implying that deceleration of subsidence was related to interaction between volcanic thermal activity and the aquifer.

Modelling spatiotemporal change using multidimensional arrays Meng

NASA Astrophysics Data System (ADS)

Lu, Meng; Appel, Marius; Pebesma, Edzer

2017-04-01

The large variety of remote sensors, model simulations, and in-situ records provide great opportunities to model environmental change. The massive amount of high-dimensional data calls for methods to integrate data from various sources and to analyse spatiotemporal and thematic information jointly. An array is a collection of elements ordered and indexed in arbitrary dimensions, which naturally represent spatiotemporal phenomena that are identified by their geographic locations and recording time. In addition, array regridding (e.g., resampling, down-/up-scaling), dimension reduction, and spatiotemporal statistical algorithms are readily applicable to arrays. However, the role of arrays in big geoscientific data analysis has not been systematically studied: How can arrays discretise continuous spatiotemporal phenomena? How can arrays facilitate the extraction of multidimensional information? How can arrays provide a clean, scalable and reproducible change modelling process that is communicable between mathematicians, computer scientist, Earth system scientist and stakeholders? This study emphasises on detecting spatiotemporal change using satellite image time series. Current change detection methods using satellite image time series commonly analyse data in separate steps: 1) forming a vegetation index, 2) conducting time series analysis on each pixel, and 3) post-processing and mapping time series analysis results, which does not consider spatiotemporal correlations and ignores much of the spectral information. Multidimensional information can be better extracted by jointly considering spatial, spectral, and temporal information. To approach this goal, we use principal component analysis to extract multispectral information and spatial autoregressive models to account for spatial correlation in residual based time series structural change modelling. We also discuss the potential of multivariate non-parametric time series structural change methods, hierarchical modelling, and extreme event detection methods to model spatiotemporal change. We show how array operations can facilitate expressing these methods, and how the open-source array data management and analytics software SciDB and R can be used to scale the process and make it easily reproducible.

Cued Speech Transliteration: Effects of Accuracy and Lag Time on Message Intelligibility

ERIC Educational Resources Information Center

Krause, Jean C.; Lopez, Katherine A.

2017-01-01

This paper is the second in a series concerned with the level of access afforded to students who use educational interpreters. The first paper (Krause & Tessler, 2016) focused on factors affecting accuracy of messages produced by Cued Speech (CS) transliterators (expression). In this study, factors affecting intelligibility (reception by deaf…

The transcriptome of Fusarium graminearum during the infection of wheat

USDA-ARS?s Scientific Manuscript database

Fusarium graminearum causes head blight disease in wheat and barley. To help understand the infection process on wheat we studied global gene expression of F. graminearum in a time series from 24 to 196 hours after inoculation, compared to a water control. The infection is rapid and already after 48...

A method for ensemble wildland fire simulation

Treesearch

Mark A. Finney; Isaac C. Grenfell; Charles W. McHugh; Robert C. Seli; Diane Trethewey; Richard D. Stratton; Stuart Brittain

2011-01-01

An ensemble simulation system that accounts for uncertainty in long-range weather conditions and two-dimensional wildland fire spread is described. Fuel moisture is expressed based on the energy release component, a US fire danger rating index, and its variation throughout the fire season is modeled using time series analysis of historical weather data. This analysis...

A Microplate Reader-Based System for Visualizing Transcriptional Activity During in vivo Microbial Interactions in Space and Time.

PubMed

Hennessy, Rosanna C; Stougaard, Peter; Olsson, Stefan

2017-03-21

Here, we report the development of a microplate reader-based system for visualizing gene expression dynamics in living bacterial cells in response to a fungus in space and real-time. A bacterium expressing the red fluorescent protein mCherry fused to the promoter region of a regulator gene nunF indicating activation of an antifungal secondary metabolite gene cluster was used as a reporter system. Time-lapse image recordings of the reporter red signal and a green signal from fluorescent metabolites combined with microbial growth measurements showed that nunF-regulated gene transcription is switched on when the bacterium enters the deceleration growth phase and upon physical encounter with fungal hyphae. This novel technique enables real-time live imaging of samples by time-series multi-channel automatic recordings using a microplate reader as both an incubator and image recorder of general use to researchers. The technique can aid in deciding when to destructively sample for other methods e.g. transcriptomics and mass spectrometry imaging to study gene expression and metabolites exchanged during the interaction.

Clinicopathological correlations of Bcl-xL and Bax expression in differentiated thyroid carcinoma.

PubMed

Martínez-Brocca, M Asunción; Castilla, Carolina; Navarro, Elena; Amaya, M José; Travado, Paulino; Japón, Miguel A; Sáez, Carmen

2008-02-01

The Bcl-2 family proteins are essential mediators in the apoptotic process. Our aim was to investigate whether anti-apoptotic Bcl-xL and pro-apoptotic Bax were over-expressed in a large series of differentiated thyroid carcinomas (DTC) and to study their association with tumour presentation at diagnosis and prognosis. We examined the immunohistochemical expression of Bcl-xL and Bax in benign nodular thyroid disease (BNTD) and DTC and their association with clinicopathological parameters. Thyroid tissue samples were collected from an unselected series of patients undergoing surgical resection for DTC (n = 74) or BNTD (n = 15). Among DTC cases, expression of Bcl-xL was found to be high in 43.2% and low or absent in 56.8%. Expression of Bax was high in 75.7% and low or absent in 24.3%. Non-neoplastic thyroid tissue was largely unstained for both proteins. Among BNTD cases, expression of Bcl-xL was high in 13.3% and low or absent in 86.6%. Expression of Bax was high in 14.3% and low or absent in 86.6%. A significant association was found between Bcl-xL expression and the presence of high-risk histological subtype (P < 0.05), and regional lymph node (P < 0.01) and distant metastases (P < 0.01). The association between high Bcl-xL expression levels and a longer time of persistent disease after radioiodine ablation was also significant (P < 0.01). Bcl-xL expression was confirmed as an independent prognostic factor for persistent disease in DTC (relative risk, 2.5; 95% confidence interval, 1.1-5.9; P < 0.05). Immunohistochemical expression of Bcl-xL might be a valuable tool in the prediction of tumour aggressiveness in DTC.

MiRNA Analysis by Quantitative PCR in Preterm Human Breast Milk Reveals Daily Fluctuations of hsa-miR-16-5p.

PubMed

Floris, Ilaria; Billard, Hélène; Boquien, Clair-Yves; Joram-Gauvard, Evelyne; Simon, Laure; Legrand, Arnaud; Boscher, Cécile; Rozé, Jean-Christophe; Bolaños-Jiménez, Francisco; Kaeffer, Bertrand

2015-01-01

Human breast milk is an extremely dynamic fluid containing many biologically-active components which change throughout the feeding period and throughout the day. We designed a miRNA assay on minimized amounts of raw milk obtained from mothers of preterm infants. We investigated changes in miRNA expression within month 2 of lactation and then over the course of 24 hours. Analyses were performed on pooled breast milk, made by combining samples collected at different clock times from the same mother donor, along with time series collected over 24 hours from four unsynchronized mothers. Whole milk, lipids or skim milk fractions were processed and analyzed by qPCR. We measured hsa-miR-16-5p, hsa-miR-21-5p, hsa-miR-146-5p, and hsa-let-7a, d and g (all -5p). Stability of miRNA endogenous controls was evaluated using RefFinder, a web tool integrating geNorm, Normfinder, BestKeeper and the comparative ΔΔCt method. MiR-21 and miR-16 were stably expressed in whole milk collected within month 2 of lactation from four mothers. Analysis of lipids and skim milk revealed that miR-146b and let-7d were better references in both fractions. Time series (5H-23H) allowed the identification of a set of three endogenous reference genes (hsa-let-7d, hsa-let-7g and miR-146b) to normalize raw quantification cycle (Cq) data. We identified a daily oscillation of miR-16-5p. Our assay allows exploring miRNA levels of breast milk from mother with preterm baby collected in time series over 48-72 hours.

Accurate expressions for solar cell fill factors including series and shunt resistances

NASA Astrophysics Data System (ADS)

Green, Martin A.

2016-02-01

Together with open-circuit voltage and short-circuit current, fill factor is a key solar cell parameter. In their classic paper on limiting efficiency, Shockley and Queisser first investigated this factor's analytical properties showing, for ideal cells, it could be expressed implicitly in terms of the maximum power point voltage. Subsequently, fill factors usually have been calculated iteratively from such implicit expressions or from analytical approximations. In the absence of detrimental series and shunt resistances, analytical fill factor expressions have recently been published in terms of the Lambert W function available in most mathematical computing software. Using a recently identified perturbative relationship, exact expressions in terms of this function are derived in technically interesting cases when both series and shunt resistances are present but have limited impact, allowing a better understanding of their effect individually and in combination. Approximate expressions for arbitrary shunt and series resistances are then deduced, which are significantly more accurate than any previously published. A method based on the insights developed is also reported for deducing one-diode fits to experimental data.

A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection.

PubMed

Carey, Michelle; Ramírez, Juan Camilo; Wu, Shuang; Wu, Hulin

2018-07-01

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

Evaluating the uncertainty of predicting future climate time series at the hourly time scale

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.

2011-12-01

A stochastic downscaling methodology is developed to generate hourly, point-scale time series for several meteorological variables, such as precipitation, cloud cover, shortwave radiation, air temperature, relative humidity, wind speed, and atmospheric pressure. The methodology uses multi-model General Circulation Model (GCM) realizations and an hourly weather generator, AWE-GEN. Probabilistic descriptions of factors of change (a measure of climate change with respect to historic conditions) are computed for several climate statistics and different aggregation times using a Bayesian approach that weights the individual GCM contributions. The Monte Carlo method is applied to sample the factors of change from their respective distributions thereby permitting the generation of time series in an ensemble fashion, which reflects the uncertainty of climate projections of future as well as the uncertainty of the downscaling procedure. Applications of the methodology and probabilistic expressions of certainty in reproducing future climates for the periods, 2000 - 2009, 2046 - 2065 and 2081 - 2100, using the 1962 - 1992 period as the baseline, are discussed for the location of Firenze (Italy). The climate predictions for the period of 2000 - 2009 are tested against observations permitting to assess the reliability and uncertainties of the methodology in reproducing statistics of meteorological variables at different time scales.

Comparison between four dissimilar solar panel configurations

NASA Astrophysics Data System (ADS)

Suleiman, K.; Ali, U. A.; Yusuf, Ibrahim; Koko, A. D.; Bala, S. I.

2017-12-01

Several studies on photovoltaic systems focused on how it operates and energy required in operating it. Little attention is paid on its configurations, modeling of mean time to system failure, availability, cost benefit and comparisons of parallel and series-parallel designs. In this research work, four system configurations were studied. Configuration I consists of two sub-components arranged in parallel with 24 V each, configuration II consists of four sub-components arranged logically in parallel with 12 V each, configuration III consists of four sub-components arranged in series-parallel with 8 V each, and configuration IV has six sub-components with 6 V each arranged in series-parallel. Comparative analysis was made using Chapman Kolmogorov's method. The derivation for explicit expression of mean time to system failure, steady state availability and cost benefit analysis were performed, based on the comparison. Ranking method was used to determine the optimal configuration of the systems. The results of analytical and numerical solutions of system availability and mean time to system failure were determined and it was found that configuration I is the optimal configuration.

Visualization of time series statistical data by shape analysis (GDP ratio changes among Asia countries)

NASA Astrophysics Data System (ADS)

Shirota, Yukari; Hashimoto, Takako; Fitri Sari, Riri

2018-03-01

It has been very significant to visualize time series big data. In the paper we shall discuss a new analysis method called “statistical shape analysis” or “geometry driven statistics” on time series statistical data in economics. In the paper, we analyse the agriculture, value added and industry, value added (percentage of GDP) changes from 2000 to 2010 in Asia. We handle the data as a set of landmarks on a two-dimensional image to see the deformation using the principal components. The point of the analysis method is the principal components of the given formation which are eigenvectors of its bending energy matrix. The local deformation can be expressed as the set of non-Affine transformations. The transformations give us information about the local differences between in 2000 and in 2010. Because the non-Affine transformation can be decomposed into a set of partial warps, we present the partial warps visually. The statistical shape analysis is widely used in biology but, in economics, no application can be found. In the paper, we investigate its potential to analyse the economic data.

Deformation Along the Rio Grande Rift: Investigating the Spatial and Temporal Distribution of Strain Using GPS

NASA Astrophysics Data System (ADS)

Murray, K. D.; Murray, M. H.; Sheehan, A. F.; Nerem, R. S.

2014-12-01

Low velocity (<1 mm/yr) extensional environments, such as the Rio Grande rift (RGR) in Colorado and New Mexico, are complex but can provide insights into continental dynamics, tectonic processes, and seismic hazards. We use eight years of measurements from 26 continuous GPS stations across the RGR installed as part of a collaborative EarthScope experiment. We combine this data with regional Plate Boundary Observatory (PBO) and National Geodetic Survey (NGS) CORS GPS stations, and survey-mode data collected on NGS benchmarks to investigate how deformation is distributed across a broad area from the Great Plains to the Colorado Plateau. The data from over 150 stations are processed using GAMIT/GLOBK, and time series, velocities, strain rates are estimated with respect to realizations of a stable North America reference frame, such as NA12. This study extends our previous analysis, based on 4 years of data, which found an approximately uniform 1.2 nanostrain/yr east-west extensional strain rate across the entire region that was not concentrated on the narrow surface expression of the rift. We expand on this previous work by using a denser network of GPS stations and analyzing longer time series, which reduce horizontal velocity uncertainties to approximately 0.15 mm/yr. We also improve the accuracy of the estimated velocity uncertainties by robustly characterizing time-correlated noise. The noise models indicate that both power-law and flicker noise are present in the time series along with white noise. On average, power law noise constitutes about 90% of the total noise in the vertical component and 60% in the horizontal components for the RGR sites. We use the time series, and velocity and strain-rate estimates to constrain spatial and temporal variations in the deformation field in order to locate possible regions of strain localization and detect transient deformation signals, and to address some of the kinematic and dynamic issues raised by the observation that a broad, low seismic velocity zone underlies the narrow geologic surface expression of the RGR defined by normal fault bounded basins.

Interpretable Categorization of Heterogeneous Time Series Data

NASA Technical Reports Server (NTRS)

Lee, Ritchie; Kochenderfer, Mykel J.; Mengshoel, Ole J.; Silbermann, Joshua

2017-01-01

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Statistical modeling of isoform splicing dynamics from RNA-seq time series data.

PubMed

Huang, Yuanhua; Sanguinetti, Guido

2016-10-01

Isoform quantification is an important goal of RNA-seq experiments, yet it remains problematic for genes with low expression or several isoforms. These difficulties may in principle be ameliorated by exploiting correlated experimental designs, such as time series or dosage response experiments. Time series RNA-seq experiments, in particular, are becoming increasingly popular, yet there are no methods that explicitly leverage the experimental design to improve isoform quantification. Here, we present DICEseq, the first isoform quantification method tailored to correlated RNA-seq experiments. DICEseq explicitly models the correlations between different RNA-seq experiments to aid the quantification of isoforms across experiments. Numerical experiments on simulated datasets show that DICEseq yields more accurate results than state-of-the-art methods, an advantage that can become considerable at low coverage levels. On real datasets, our results show that DICEseq provides substantially more reproducible and robust quantifications, increasing the correlation of estimates from replicate datasets by up to 10% on genes with low or moderate expression levels (bottom third of all genes). Furthermore, DICEseq permits to quantify the trade-off between temporal sampling of RNA and depth of sequencing, frequently an important choice when planning experiments. Our results have strong implications for the design of RNA-seq experiments, and offer a novel tool for improved analysis of such datasets. Python code is freely available at http://diceseq.sf.net G.Sanguinetti@ed.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Short-term forecasting of meteorological time series using Nonparametric Functional Data Analysis (NPFDA)

NASA Astrophysics Data System (ADS)

Curceac, S.; Ternynck, C.; Ouarda, T.

2015-12-01

Over the past decades, a substantial amount of research has been conducted to model and forecast climatic variables. In this study, Nonparametric Functional Data Analysis (NPFDA) methods are applied to forecast air temperature and wind speed time series in Abu Dhabi, UAE. The dataset consists of hourly measurements recorded for a period of 29 years, 1982-2010. The novelty of the Functional Data Analysis approach is in expressing the data as curves. In the present work, the focus is on daily forecasting and the functional observations (curves) express the daily measurements of the above mentioned variables. We apply a non-linear regression model with a functional non-parametric kernel estimator. The computation of the estimator is performed using an asymmetrical quadratic kernel function for local weighting based on the bandwidth obtained by a cross validation procedure. The proximities between functional objects are calculated by families of semi-metrics based on derivatives and Functional Principal Component Analysis (FPCA). Additionally, functional conditional mode and functional conditional median estimators are applied and the advantages of combining their results are analysed. A different approach employs a SARIMA model selected according to the minimum Akaike (AIC) and Bayessian (BIC) Information Criteria and based on the residuals of the model. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE), relative RMSE, BIAS and relative BIAS. The results indicate that the NPFDA models provide more accurate forecasts than the SARIMA models. Key words: Nonparametric functional data analysis, SARIMA, time series forecast, air temperature, wind speed

Applying dynamic Bayesian networks to perturbed gene expression data.

PubMed

Dojer, Norbert; Gambin, Anna; Mizera, Andrzej; Wilczyński, Bartek; Tiuryn, Jerzy

2006-05-08

A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments. We extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed. We show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough.

Clustering change patterns using Fourier transformation with time-course gene expression data.

PubMed

Kim, Jaehee

2011-01-01

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a period of time because biologically related gene groups can share the same change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. This work is aimed at discovering gene groups with similar change patterns which share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. We applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns.

Egr-1: A Candidate Transcription Factor Involved in Molecular Processes Underlying Time-Memory.

PubMed

Shah, Aridni; Jain, Rikesh; Brockmann, Axel

2018-01-01

In honey bees, continuous foraging is accompanied by a sustained up-regulation of the immediate early gene Egr-1 (early growth response protein-1) and candidate downstream genes involved in learning and memory. Here, we present a series of feeder training experiments indicating that Egr-1 expression is highly correlated with the time and duration of training even in the absence of the food reward. Foragers that were trained to visit a feeder over the whole day and then collected on a day without food presentation showed Egr-1 up-regulation over the whole day with a peak expression around 14:00. When exposed to a time-restricted feeder presentation, either 2 h in the morning or 2 h in the evening, Egr-1 expression in the brain was up-regulated only during the hours of training. Foragers that visited a feeder in the morning as well as in the evening showed two peaks of Egr-1 expression. Finally, when we prevented time-trained foragers from leaving the colony using artificial rain, Egr-1 expression in the brains was still slightly but significantly up-regulated around the time of feeder training. In situ hybridization studies showed that active foraging and time-training induced Egr-1 up-regulation occurred in the same brain areas, preferentially the small Kenyon cells of the mushroom bodies and the antennal and optic lobes. Based on these findings we propose that foraging induced Egr-1 expression can get regulated by the circadian clock after time-training over several days and Egr-1 is a candidate transcription factor involved in molecular processes underlying time-memory.

Differentially Expressed microRNAs and Target Genes Associated with Plastic Internode Elongation in Alternanthera philoxeroides in Contrasting Hydrological Habitats

PubMed Central

Li, Gengyun; Deng, Ying; Geng, Yupeng; Zhou, Chengchuan; Wang, Yuguo; Zhang, Wenju; Song, Zhiping; Gao, Lexuan; Yang, Ji

2017-01-01

Phenotypic plasticity is crucial for plants to survive in changing environments. Discovering microRNAs, identifying their targets and further inferring microRNA functions in mediating plastic developmental responses to environmental changes have been a critical strategy for understanding the underlying molecular mechanisms of phenotypic plasticity. In this study, the dynamic expression patterns of microRNAs under contrasting hydrological habitats in the amphibious species Alternanthera philoxeroides were identified by time course expression profiling using high-throughput sequencing technology. A total of 128 known and 18 novel microRNAs were found to be differentially expressed under contrasting hydrological habitats. The microRNA:mRNA pairs potentially associated with plastic internode elongation were identified by integrative analysis of microRNA and mRNA expression profiles, and were validated by qRT-PCR and 5′ RLM-RACE. The results showed that both the universal microRNAs conserved across different plants and the unique microRNAs novelly identified in A. philoxeroides were involved in the responses to varied water regimes. The results also showed that most of the differentially expressed microRNAs were transiently up-/down-regulated at certain time points during the treatments. The fine-scale temporal changes in microRNA expression highlighted the importance of time-series sampling in identifying stress-responsive microRNAs and analyzing their role in stress response/tolerance. PMID:29259617

Identification of Development-Related Genes in the Ovaries of Adult Harmonia axyridis (Pallas) Lady Beetles Using a Time- Series Analysis by RNA-seq.

PubMed

Du, Wenxiao; Zeng, Fanrong

2016-12-14

Adults of the lady beetle species Harmonia axyridis (Pallas) are bred artificially en masse for classic biological control, which requires egg-laying by the H. axyridis ovary. Development-related genes may impact the growth of the H. axyridis adult ovary but have not been reported. Here, we used integrative time-series RNA-seq analysis of the ovary in H. axyridis adults to detect development-related genes. A total of 28,558 unigenes were functionally annotated using seven types of databases to obtain an annotated unigene database for ovaries in H. axyridis adults. We also analysed differentially expressed genes (DEGs) between samples. Based on a combination of the results of this bioinformatics analysis with literature reports and gene expression level changes in four different stages, we focused on the development of oocyte reproductive stem cell and yolk formation process and identified 26 genes with high similarity to development-related genes. 20 DEGs were randomly chosen for quantitative real-time PCR (qRT-PCR) to validate the accuracy of the RNA-seq results. This study establishes a robust pipeline for the discovery of key genes using high-throughput sequencing and the identification of a class of development-related genes for characterization.

[The calming process of anger experience: time series changes of affects, cognitions, and behaviors].

PubMed

Hibino, Kei; Yukawa, Shintaro

2004-02-01

This study investigated time series changes and relationships of affects, cognitions, and behaviors immediately, a few days, and a week after anger episodes. Two hundred undergraduates (96 men, and 104 women) completed a questionnaire. The results were as follows. Anger intensely aroused immediately after anger episodes, and was rapidly calmed as time passed. Anger and depression correlated in each period, so depression was accompanied with anger experiences. The results of covariance structure analysis showed that aggressive behavior was evoked only by affects (especially anger) immediately, and was evoked only by cognitions (especially inflating) a few days after the episode. One week after the episode, aggressive behavior decreased, and was not influenced by affects and cognitions. Anger elicited all anger-expressive behaviors such as aggressive behavior, social sharing, and object-displacement, while depression accompanied with anger episodes elicited only object-displacement.

Spatio-Temporal Video Segmentation with Shape Growth or Shrinkage Constraint

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Charpiat, Guillaume; Brucker, Ludovic; Menze, Bjoern H.

2014-01-01

We propose a new method for joint segmentation of monotonously growing or shrinking shapes in a time sequence of noisy images. The task of segmenting the image time series is expressed as an optimization problem using the spatio-temporal graph of pixels, in which we are able to impose the constraint of shape growth or of shrinkage by introducing monodirectional infinite links connecting pixels at the same spatial locations in successive image frames. The globally optimal solution is computed with a graph cut. The performance of the proposed method is validated on three applications: segmentation of melting sea ice floes and of growing burned areas from time series of 2D satellite images, and segmentation of a growing brain tumor from sequences of 3D medical scans. In the latter application, we impose an additional intersequences inclusion constraint by adding directed infinite links between pixels of dependent image structures.

The annual cycles of phytoplankton biomass

USGS Publications Warehouse

Winder, M.; Cloern, J.E.

2010-01-01

Terrestrial plants are powerful climate sentinels because their annual cycles of growth, reproduction and senescence are finely tuned to the annual climate cycle having a period of one year. Consistency in the seasonal phasing of terrestrial plant activity provides a relatively low-noise background from which phenological shifts can be detected and attributed to climate change. Here, we ask whether phytoplankton biomass also fluctuates over a consistent annual cycle in lake, estuarine-coastal and ocean ecosystems and whether there is a characteristic phenology of phytoplankton as a consistent phase and amplitude of variability. We compiled 125 time series of phytoplankton biomass (chloro-phyll a concentration) from temperate and subtropical zones and used wavelet analysis to extract their dominant periods of variability and the recurrence strength at those periods. Fewer than half (48%) of the series had a dominant 12-month period of variability, commonly expressed as the canonical spring-bloom pattern. About 20 per cent had a dominant six-month period of variability, commonly expressed as the spring and autumn or winter and summer blooms of temperate lakes and oceans. These annual patterns varied in recurrence strength across sites, and did not persist over the full series duration at some sites. About a third of the series had no component of variability at either the six-or 12-month period, reflecting a series of irregular pulses of biomass. These findings show that there is high variability of annual phytoplankton cycles across ecosystems, and that climate-driven annual cycles can be obscured by other drivers of population variability, including human disturbance, aperiodic weather events and strong trophic coupling between phytoplankton and their consumers. Regulation of phytoplankton biomass by multiple processes operating at multiple time scales adds complexity to the challenge of detecting climate-driven trends in aquatic ecosystems where the noise to signal ratio is high. ?? 2010 The Royal Society.

MicroRNAs 142-3p, miR-155 and miR-203 Are Deregulated in Gastric MALT Lymphomas Compared to Chronic Gastritis.

PubMed

Fernández, Concepción; Bellosillo, Beatriz; Ferraro, Mariana; Seoane, Agustín; Sánchez-González, Blanca; Pairet, Silvia; Pons, Aina; Barranco, Luis; Vela, María Carmen; Gimeno, Eva; Colomo, Lluís; Besses, Carles; Navarro, Alfons; Salar, Antonio

2017-01-02

Over the last years, our knowledge on pathogenesis of gastric MALT lymphoma has greatly improved, but its morphological diagnosis is still hampered by overlapping histological features with advanced chronic gastritis. MicroRNAs are deregulated in lymphomas, but their role and usefulness in gastric MALT lymphoma has not been extensively investigated. We analyzed the expression of 384 miRNAs using TaqMan microRNA assay in a training series of 10 gastric MALT lymphomas, 3 chronic gastritis and 2 reactive lymph nodes. Then, significantly deregulated miRNAs were individually assessed by real-time PCR in a validation series of 16 gastric MALT lymphomas and 12 chronic gastritis. Gastric MALT lymphoma is characterized by a specific miRNA expression profile. Among the differentially expressed miRNAs, a significant overexpression of miR-142-3p and miR-155 and down-regulation of miR-203 was observed in gastric MALT lymphoma when compared to chronic gastritis. miR-142-3p, miR-155 and miR-203 expression levels might be helpful biomarkers for the differential diagnosis between gastric MALT lymphomas and chronic gastritis. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Social Risk and Depression: Evidence from Manual and Automatic Facial Expression Analysis

PubMed Central

Girard, Jeffrey M.; Cohn, Jeffrey F.; Mahoor, Mohammad H.; Mavadati, Seyedmohammad; Rosenwald, Dean P.

2014-01-01

Investigated the relationship between change over time in severity of depression symptoms and facial expression. Depressed participants were followed over the course of treatment and video recorded during a series of clinical interviews. Facial expressions were analyzed from the video using both manual and automatic systems. Automatic and manual coding were highly consistent for FACS action units, and showed similar effects for change over time in depression severity. For both systems, when symptom severity was high, participants made more facial expressions associated with contempt, smiled less, and those smiles that occurred were more likely to be accompanied by facial actions associated with contempt. These results are consistent with the “social risk hypothesis” of depression. According to this hypothesis, when symptoms are severe, depressed participants withdraw from other people in order to protect themselves from anticipated rejection, scorn, and social exclusion. As their symptoms fade, participants send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and produced the same pattern of depression effects suggests that automatic facial expression analysis may be ready for use in behavioral and clinical science. PMID:24598859

The gravitational potential due to uniform disks and rings

NASA Astrophysics Data System (ADS)

Lass, H.; Blitzer, L.

1983-07-01

The gravitational potential of bodies possessing axial symmetry can be expressed as a power series in distance, with the Legendre polynomials as coefficients. Such series, however, converge so slowly in the neighborhood of thin, uniform disks and rings that too many series terms must be summed in order to obtain an accurate field measure. A gravitational potential expression is presently obtained in closed form, in terms of complete elliptic integrals.

The time-local view of nonequilibrium statistical mechanics. I. Linear theory of transport and relaxation

NASA Astrophysics Data System (ADS)

der, R.

1987-01-01

The various approaches to nonequilibrium statistical mechanics may be subdivided into convolution and convolutionless (time-local) ones. While the former, put forward by Zwanzig, Mori, and others, are used most commonly, the latter are less well developed, but have proven very useful in recent applications. The aim of the present series of papers is to develop the time-local picture (TLP) of nonequilibrium statistical mechanics on a new footing and to consider its physical implications for topics such as the formulation of irreversible thermodynamics. The most natural approach to TLP is seen to derive from the Fourier-Laplace transformwidetilde{C}(z)) of pertinent time correlation functions, which on the physical sheet typically displays an essential singularity at z=∞ and a number of macroscopic and microscopic poles in the lower half-plane corresponding to long- and short-lived modes, respectively, the former giving rise to the autonomous macrodynamics, whereas the latter are interpreted as doorway modes mediating the transfer of information from relevant to irrelevant channels. Possible implications of this doorway mode concept for socalled extended irreversible thermodynamics are briefly discussed. The pole structure is used for deriving new kinds of generalized Green-Kubo relations expressing macroscopic quantities, transport coefficients, e.g., by contour integrals over current-current correlation functions obeying Hamiltonian dynamics, the contour integration replacing projection. The conventional Green-Kubo relations valid for conserved quantities only are rederived for illustration. Moreover,widetilde{C}(z) may be expressed by a Laurent series expansion in positive and negative powers of z, from which a rigorous, general, and straightforward method is developed for extracting all macroscopic quantities from so-called secularly divergent expansions ofwidetilde{C}(z) as obtained from the application of conventional many-body techniques to the calculation ofwidetilde{C}(z). The expressions are formulated as time scale expansions, which should rapidly converge if macroscopic and microscopic time scales are sufficiently well separated, i.e., if lifetime ("memory") effects are not too large.

Discovering monotonic stemness marker genes from time-series stem cell microarray data.

PubMed

Wang, Hsei-Wei; Sun, Hsing-Jen; Chang, Ting-Yu; Lo, Hung-Hao; Cheng, Wei-Chung; Tseng, George C; Lin, Chin-Teng; Chang, Shing-Jyh; Pal, Nikhil; Chung, I-Fang

2015-01-01

Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics. We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages. We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/.

Large deviation probabilities for correlated Gaussian stochastic processes and daily temperature anomalies

NASA Astrophysics Data System (ADS)

Massah, Mozhdeh; Kantz, Holger

2016-04-01

As we have one and only one earth and no replicas, climate characteristics are usually computed as time averages from a single time series. For understanding climate variability, it is essential to understand how close a single time average will typically be to an ensemble average. To answer this question, we study large deviation probabilities (LDP) of stochastic processes and characterize them by their dependence on the time window. In contrast to iid variables for which there exists an analytical expression for the rate function, the correlated variables such as auto-regressive (short memory) and auto-regressive fractionally integrated moving average (long memory) processes, have not an analytical LDP. We study LDP for these processes, in order to see how correlation affects this probability in comparison to iid data. Although short range correlations lead to a simple correction of sample size, long range correlations lead to a sub-exponential decay of LDP and hence to a very slow convergence of time averages. This effect is demonstrated for a 120 year long time series of daily temperature anomalies measured in Potsdam (Germany).

Combined temperature and density series for fluid-phase properties. I. Square-well spheres

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elliott, J. Richard; Schultz, Andrew J.; Kofke, David A.

Cluster integrals are evaluated for the coefficients of the combined temperature- and density-expansion of pressure: Z = 1 + B{sub 2}(β) η + B{sub 3}(β) η{sup 2} + B{sub 4}(β) η{sup 3} + ⋯, where Z is the compressibility factor, η is the packing fraction, and the B{sub i}(β) coefficients are expanded as a power series in reciprocal temperature, β, about β = 0. The methodology is demonstrated for square-well spheres with λ = [1.2-2.0], where λ is the well diameter relative to the hard core. For this model, the B{sub i} coefficients can be expressed in closed form asmore » a function of β, and we develop appropriate expressions for i = 2-6; these expressions facilitate derivation of the coefficients of the β series. Expanding the B{sub i} coefficients in β provides a correspondence between the power series in density (typically called the virial series) and the power series in β (typically called thermodynamic perturbation theory, TPT). The coefficients of the β series result in expressions for the Helmholtz energy that can be compared to recent computations of TPT coefficients to fourth order in β. These comparisons show good agreement at first order in β, suggesting that the virial series converges for this term. Discrepancies for higher-order terms suggest that convergence of the density series depends on the order in β. With selection of an appropriate approximant, the treatment of Helmholtz energy that is second order in β appears to be stable and convergent at least to the critical density, but higher-order coefficients are needed to determine how far this behavior extends into the liquid.« less

Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study.

PubMed

Fan, Ming; Kuwahara, Hiroyuki; Wang, Xiaolei; Wang, Suojin; Gao, Xin

2015-11-01

Parameter estimation is a challenging computational problem in the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter estimation of gene circuit models from such time-series mRNA data has become an important method for quantitatively dissecting the regulation of gene expression. By focusing on the modeling of gene circuits, we examine here the performance of three types of state-of-the-art parameter estimation methods: population-based methods, online methods and model-decomposition-based methods. Our results show that certain population-based methods are able to generate high-quality parameter solutions. The performance of these methods, however, is heavily dependent on the size of the parameter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, online methods and model decomposition-based methods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fast methods with local search as a subsequent refinement procedure can substantially increase the quality of their parameter estimates to the level on par with the best solution obtained from the population-based methods while maintaining high computational speed. These suggest that such hybrid methods can be a promising alternative to the more commonly used population-based methods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatory mechanisms makes the size of the parameter search space vastly large. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Large Field Visualization with Demand-Driven Calculation

NASA Technical Reports Server (NTRS)

Moran, Patrick J.; Henze, Chris

1999-01-01

We present a system designed for the interactive definition and visualization of fields derived from large data sets: the Demand-Driven Visualizer (DDV). The system allows the user to write arbitrary expressions to define new fields, and then apply a variety of visualization techniques to the result. Expressions can include differential operators and numerous other built-in functions, ail of which are evaluated at specific field locations completely on demand. The payoff of following a demand-driven design philosophy throughout becomes particularly evident when working with large time-series data, where the costs of eager evaluation alternatives can be prohibitive.

The Chern-Simons Current in Systems of DNA-RNA Transcriptions

NASA Astrophysics Data System (ADS)

Capozziello, Salvatore; Pincak, Richard; Kanjamapornkul, Kabin; Saridakis, Emmanuel N.

2018-04-01

A Chern-Simons current, coming from ghost and anti-ghost fields of supersymmetry theory, can be used to define a spectrum of gene expression in new time series data where a spinor field, as alternative representation of a gene, is adopted instead of using the standard alphabet sequence of bases $A, T, C, G, U$. After a general discussion on the use of supersymmetry in biological systems, we give examples of the use of supersymmetry for living organism, discuss the codon and anti-codon ghost fields and develop an algebraic construction for the trash DNA, the DNA area which does not seem active in biological systems. As a general result, all hidden states of codon can be computed by Chern-Simons 3 forms. Finally, we plot a time series of genetic variations of viral glycoprotein gene and host T-cell receptor gene by using a gene tensor correlation network related to the Chern-Simons current. An empirical analysis of genetic shift, in host cell receptor genes with separated cluster of gene and genetic drift in viral gene, is obtained by using a tensor correlation plot over time series data derived as the empirical mode decomposition of Chern-Simons current.

Understanding characteristics in multivariate traffic flow time series from complex network structure

NASA Astrophysics Data System (ADS)

Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

2017-07-01

Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

The Chern-Simons current in time series of knots and links in proteins

NASA Astrophysics Data System (ADS)

Capozziello, Salvatore; Pincak, Richard

2018-06-01

A superspace model of knots and links for DNA time series data is proposed to take into account the feedback loop from docking to undocking state of protein-protein interactions. In particular, the direction of interactions between the 8 hidden states of DNA is considered. It is a E8 ×E8 unified spin model where the genotype, from active and inactive side of DNA time data series, can be considered for any living organism. The mathematical model is borrowed from loop-quantum gravity and adapted to biology. It is used to derive equations for gene expression describing transitions from ground to excited states, and for the 8 coupling states between geneon and anti-geneon transposon and retrotransposon in trash DNA. Specifically, we adopt a modified Grothendieck cohomology and a modified Khovanov cohomology for biology. The result is a Chern-Simons current in (8 + 3) extradimensions of a given unoriented supermanifold with ghost fields of protein structures. The 8 dimensions come from the 8 hidden states of spinor field of genetic code. The extradimensions come from the 3 types of principle fiber bundle in the secondary protein.

Cloning of peroxisome proliferators activated receptors in the cobia (Rachycentron canadum) and their expression at different life-cycle stages under cage aquaculture.

PubMed

Tsai, Mei-Ling; Chen, Houng-Yung; Tseng, Mei-Cheuh; Chang, Rey-Chang

2008-12-01

We present the cDNA sequences and tissue mRNA expression of peroxisome proliferator-activated receptor (PPAR) alpha, beta and gamma isotypes in the cobia (Rachycentron canadum), a warm water pelagic fish that is becoming a fish of choice for offshore cage farming. RT-PCR and real-time PCR showed that PPARalpha mRNA predominated in red muscle, heart and liver whereas PPARbeta was expressed mainly in liver and pyloric caeca. In contrast, PPARgamma transcripts were detected in all of the tissues examined, with the highest level occurring in visceral fat depot. Our 52-wk time-series investigation showed that while the mRNA expression of PPARgamma in the cobia was positively (P < 0.05) related to its body lipid deposition, a negative (P < 0.05) relationship was found between PPARalpha expression in the liver and body lipid deposition. There was a significant increase in body lipid deposition and hepatic PPARgamma expression as the fish grew. The hepatic PPARgamma expression could be a sufficient parameter describing the bodily expression of PPARgamma because of its positive correlation with PPARgamma expressions in all other tissues. These results showed that PPARgamma and alpha played a pivotal role in the control of lipid metabolic and storage functions in the liver, muscle and visceral fat depot of the cobia.

Scale and time dependence of serial correlations in word-length time series of written texts

NASA Astrophysics Data System (ADS)

Rodriguez, E.; Aguilar-Cornejo, M.; Femat, R.; Alvarez-Ramirez, J.

2014-11-01

This work considered the quantitative analysis of large written texts. To this end, the text was converted into a time series by taking the sequence of word lengths. The detrended fluctuation analysis (DFA) was used for characterizing long-range serial correlations of the time series. To this end, the DFA was implemented within a rolling window framework for estimating the variations of correlations, quantified in terms of the scaling exponent, strength along the text. Also, a filtering derivative was used to compute the dependence of the scaling exponent relative to the scale. The analysis was applied to three famous English-written literary narrations; namely, Alice in Wonderland (by Lewis Carrol), Dracula (by Bram Stoker) and Sense and Sensibility (by Jane Austen). The results showed that high correlations appear for scales of about 50-200 words, suggesting that at these scales the text contains the stronger coherence. The scaling exponent was not constant along the text, showing important variations with apparent cyclical behavior. An interesting coincidence between the scaling exponent variations and changes in narrative units (e.g., chapters) was found. This suggests that the scaling exponent obtained from the DFA is able to detect changes in narration structure as expressed by the usage of words of different lengths.

Organics removal from landfill leachate and activated sludge production in SBR reactors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klimiuk, Ewa; Kulikowska, Dorota

2006-07-01

This study is aimed at estimating organic compounds removal and sludge production in SBR during treatment of landfill leachate. Four series were performed. At each series, experiments were carried out at the hydraulic retention time (HRT) of 12, 6, 3 and 2 d. The series varied in SBR filling strategies, duration of the mixing and aeration phases, and the sludge age. In series 1 and 2 (a short filling period, mixing and aeration phases in the operating cycle), the relationship between organics concentration (COD) in the leachate treated and HRT was pseudo-first-order kinetics. In series 3 (with mixing and aerationmore » phases) and series 4 (only aeration phase) with leachate supplied by means of a peristaltic pump for 4 h of the cycle (filling during reaction period) - this relationship was zero-order kinetics. Activated sludge production expressed as the observed coefficient of biomass production (Y {sub obs}) decreased correspondingly with increasing HRT. The smallest differences between reactors were observed in series 3 in which Y {sub obs} was almost stable (0.55-0.6 mg VSS/mg COD). The elimination of the mixing phase in the cycle (series 4) caused the Y {sub obs} to decrease significantly from 0.32 mg VSS/mg COD at HRT 2 d to 0.04 mg VSS/mg COD at HRT 12 d. The theoretical yield coefficient Y accounted for 0.534 mg VSS/mg COD (series 1) and 0.583 mg VSS/mg COD (series 2). In series 3 and 4, it was almost stable (0.628 mg VSS/mg COD and 0.616 mg VSS/mg COD, respectively). After the elimination of the mixing phase in the operating cycle, the specific biomass decay rate increased from 0.006 d{sup -1} (series 3) to 0.032 d{sup -1} (series 4). The operating conditions employing mixing/aeration or only aeration phases enable regulation of the sludge production. The SBRs operated under aerobic conditions are more favourable at a short hydraulic retention time. At long hydraulic retention time, it can lead to a decrease in biomass concentration in the SBR as a result of cell decay. On the contrary, in the activated sludge at long HRT, a short filling period and operating cycle of the reactor with the mixing and aeration phases seem the most favourable.« less

General Series Solutions for Stresses and Displacements in an Inner-fixed Ring

NASA Astrophysics Data System (ADS)

Jiao, Yongshu; Liu, Shuo; Qi, Dexuan

2018-03-01

The general series solution approach is provided to get the stress and displacement fields in the inner-fixed ring. After choosing an Airy stress function in series form, stresses are expressed by infinite coefficients. Displacements are obtained by integrating the geometric equations. For an inner-fixed ring, the arbitrary loads acting on outer edge are extended into two sets of Fourier series. The zero displacement boundary conditions on inner surface are utilized. Then the stress (and displacement) coefficients are expressed by loading coefficients. A numerical example shows the validity of this approach.

Manufactured aluminum oxide nanoparticles decrease expression of tight junction proteins in brain vasculature.

PubMed

Chen, Lei; Yokel, Robert A; Hennig, Bernhard; Toborek, Michal

2008-12-01

Manufactured nanoparticles of aluminum oxide (nano-alumina) have been widely used in the environment; however, their potential toxicity provides a growing concern for human health. The present study focuses on the hypothesis that nano-alumina can affect the blood-brain barrier and induce endothelial toxicity. In the first series of experiments, human brain microvascular endothelial cells (HBMEC) were exposed to alumina and control nanoparticles in dose- and time-responsive manners. Treatment with nano-alumina markedly reduced HBMEC viability, altered mitochondrial potential, increased cellular oxidation, and decreased tight junction protein expression as compared to control nanoparticles. Alterations of tight junction protein levels were prevented by cellular enrichment with glutathione. In the second series of experiments, rats were infused with nano-alumina at the dose of 29 mg/kg and the brains were stained for expression of tight junction proteins. Treatment with nano-alumina resulted in a marked fragmentation and disruption of integrity of claudin-5 and occludin. These results indicate that cerebral vasculature can be affected by nano-alumina. In addition, our data indicate that alterations of mitochondrial functions may be the underlying mechanism of nano-alumina toxicity.

Optimal Reorganization of NASA Earth Science Data for Enhanced Accessibility and Usability for the Hydrology Community

NASA Technical Reports Server (NTRS)

Teng, William; Rui, Hualan; Strub, Richard; Vollmer, Bruce

2016-01-01

A long-standing "Digital Divide" in data representation exists between the preferred way of data access by the hydrology community and the common way of data archival by earth science data centers. Typically, in hydrology, earth surface features are expressed as discrete spatial objects (e.g., watersheds), and time-varying data are contained in associated time series. Data in earth science archives, although stored as discrete values (of satellite swath pixels or geographical grids), represent continuous spatial fields, one file per time step. This Divide has been an obstacle, specifically, between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. and NASA earth science data systems. In essence, the way data are archived is conceptually orthogonal to the desired method of access. Our recent work has shown an optimal method of bridging the Divide, by enabling operational access to long-time series (e.g., 36 years of hourly data) of selected NASA datasets. These time series, which we have termed "data rods," are pre-generated or generated on-the-fly. This optimal solution was arrived at after extensive investigations of various approaches, including one based on "data curtains." The on-the-fly generation of data rods uses "data cubes," NASA Giovanni, and parallel processing. The optimal reorganization of NASA earth science data has significantly enhanced the access to and use of the data for the hydrology user community.

Wetting and drying of soil in response to precipitation: Data analysis, modeling, and forecasting

USGS Publications Warehouse

Basak, Aniruddha; Kulkarni, Chinmay; Schmidt, Kevin M.; Mengshoel, Ole

2016-01-01

This paper investigates methods to analyze and forecast soil moisture time series. We extend an existing Antecedent Water Index (AWI) model, which expresses soil moisture as a function of time and rainfall. Unfortunately, the existing AWI model does not forecast effectively for time periods beyond a few hours. To overcome this limitation, we develop a novel AWI-based model. Our model accumulates rainfall over a time interval and can fit a diverse range of wetting and drying curves. In addition, parameters in our model reflect hydrologic redistribution processes of gravity and suction.We validate our models using experimental soil moisture and rainfall time series data collected from steep gradient post-wildfire sites in Southern California, where rapid landscape change was observed in response to small to moderate rain storms. We found that our novel model fits the data for three distinct soil textures, occurring at different depths below the ground surface (5, 15, and 30 cm). Our model also successfully forecasts soil moisture trends, such as drying and wetting rate.

Subordinated continuous-time AR processes and their application to modeling behavior of mechanical system

NASA Astrophysics Data System (ADS)

Gajda, Janusz; Wyłomańska, Agnieszka; Zimroz, Radosław

2016-12-01

Many real data exhibit behavior adequate to subdiffusion processes. Very often it is manifested by so-called ;trapping events;. The visible evidence of subdiffusion we observe not only in financial time series but also in technical data. In this paper we propose a model which can be used for description of such kind of data. The model is based on the continuous time autoregressive time series with stable noise delayed by the infinitely divisible inverse subordinator. The proposed system can be applied to real datasets with short-time dependence, visible jumps and mentioned periods of stagnation. In this paper we extend the theoretical considerations in analysis of subordinated processes and propose a new model that exhibits mentioned properties. We concentrate on the main characteristics of the examined subordinated process expressed mainly in the language of the measures of dependence which are main tools used in statistical investigation of real data. We present also the simulation procedure of the considered system and indicate how to estimate its parameters. The theoretical results we illustrate by the analysis of real technical data.

EMAP and EMAGE: a framework for understanding spatially organized data.

PubMed

Baldock, Richard A; Bard, Jonathan B L; Burger, Albert; Burton, Nicolas; Christiansen, Jeff; Feng, Guanjie; Hill, Bill; Houghton, Derek; Kaufman, Matthew; Rao, Jianguo; Sharpe, James; Ross, Allyson; Stevenson, Peter; Venkataraman, Shanmugasundaram; Waterhouse, Andrew; Yang, Yiya; Davidson, Duncan R

2003-01-01

The Edinburgh MouseAtlas Project (EMAP) is a time-series of mouse-embryo volumetric models. The models provide a context-free spatial framework onto which structural interpretations and experimental data can be mapped. This enables collation, comparison, and query of complex spatial patterns with respect to each other and with respect to known or hypothesized structure. The atlas also includes a time-dependent anatomical ontology and mapping between the ontology and the spatial models in the form of delineated anatomical regions or tissues. The models provide a natural, graphical context for browsing and visualizing complex data. The Edinburgh Mouse Atlas Gene-Expression Database (EMAGE) is one of the first applications of the EMAP framework and provides a spatially mapped gene-expression database with associated tools for data mapping, submission, and query. In this article, we describe the underlying principles of the Atlas and the gene-expression database, and provide a practical introduction to the use of the EMAP and EMAGE tools, including use of new techniques for whole body gene-expression data capture and mapping.

Psychometric challenges and proposed solutions when scoring facial emotion expression codes.

PubMed

Olderbak, Sally; Hildebrandt, Andrea; Pinkpank, Thomas; Sommer, Werner; Wilhelm, Oliver

2014-12-01

Coding of facial emotion expressions is increasingly performed by automated emotion expression scoring software; however, there is limited discussion on how best to score the resulting codes. We present a discussion of facial emotion expression theories and a review of contemporary emotion expression coding methodology. We highlight methodological challenges pertinent to scoring software-coded facial emotion expression codes and present important psychometric research questions centered on comparing competing scoring procedures of these codes. Then, on the basis of a time series data set collected to assess individual differences in facial emotion expression ability, we derive, apply, and evaluate several statistical procedures, including four scoring methods and four data treatments, to score software-coded emotion expression data. These scoring procedures are illustrated to inform analysis decisions pertaining to the scoring and data treatment of other emotion expression questions and under different experimental circumstances. Overall, we found applying loess smoothing and controlling for baseline facial emotion expression and facial plasticity are recommended methods of data treatment. When scoring facial emotion expression ability, maximum score is preferred. Finally, we discuss the scoring methods and data treatments in the larger context of emotion expression research.

Series of Reciprocal Triangular Numbers

ERIC Educational Resources Information Center

Bruckman, Paul; Dence, Joseph B.; Dence, Thomas P.; Young, Justin

2013-01-01

Reciprocal triangular numbers have appeared in series since the very first infinite series were summed. Here we attack a number of subseries of the reciprocal triangular numbers by methodically expressing them as integrals.

Current molecular profile of juvenile nasopharyngeal angiofibroma: First comprehensive study from India.

PubMed

Pandey, Praveen; Mishra, Anupam; Tripathi, Ashoak Mani; Verma, Veerendra; Trivedi, Ritu; Singh, Hitendra Prakash; Kumar, Sunil; Patel, Brijesh; Singh, Vinay; Pandey, Shivani; Pandey, Amita; Mishra, Subhash Chandra

2017-03-01

An attempt is made to analyze the molecular behavior of juvenile nasopharyngeal angiofibroma (JNA). Case Series METHODS: Quantification of mRNAs expression was undertaken through real-time polymerase chain reaction in JNA (9-24) samples for VEGF-A, basic fibroblast growth factor (b-FGF), platelet-derived growth factor PDGF-A, KIT proto-oncogene receptor tyrosine kinase (c-Kit), Avian myelomatosis viral oncogene homolog (c-Myc), Harvey rat sarcoma viral oncogene homolog (H-Ras), tumor suppressor gene TP53, and androgen receptor and interleukin 6 (IL-6). The β-catenin expression was evaluated by western blot in 16 samples. Nasal polyp was taken as control. A significantly increased (P < 0.01) expression of c-myc, VEGFA, bFGF, PDGFA, c-kit, and TP53 was seen, along with enhanced expression of β-catenin. A massive enhancement of H-Ras expression was seen for the first time. Androgen receptor expression was no different, whereas IL-6 despite showing upregulation trend was not significant. The enhanced expressions of various markers suggest their potential role in JNA. Although the biological significance of c-kit, c-myc, and one of the novel markers H-Ras has yet to be defined, their significant expression may have a therapeutic importance. NA. Laryngoscope, 127:E100-E106, 2017. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

ctsGE-clustering subgroups of expression data.

PubMed

Sharabi-Schwager, Michal; Or, Etti; Ophir, Ron

2017-07-01

A pre-requisite to clustering noisy data, such as gene-expression data, is the filtering step. As an alternative to this step, the ctsGE R-package applies a sorting step in which all of the data are divided into small groups. The groups are divided according to how the time points are related to the time-series median. Then clustering is performed separately on each group. Thus, the clustering is done in two steps. First, an expression index (i.e. a sequence of 1, -1 and 0) is defined and genes with the same index are grouped together, and then each group of genes is clustered by k-means to create subgroups. The ctsGE package also provides an interactive tool to visualize and explore the gene-expression patterns and their subclusters. ctsGE proposes a way of organizing and exploring expression data without eliminating valuable information. Freely available as part of the Bioconductor project at https://bioconductor.org/packages/ctsGE/ . ron@agri.gov.il. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Mothers' pupillary responses to infant facial expressions.

PubMed

Yrttiaho, Santeri; Niehaus, Dana; Thomas, Eileen; Leppänen, Jukka M

2017-02-06

Human parental care relies heavily on the ability to monitor and respond to a child's affective states. The current study examined pupil diameter as a potential physiological index of mothers' affective response to infant facial expressions. Pupillary time-series were measured from 86 mothers of young infants in response to an array of photographic infant faces falling into four emotive categories based on valence (positive vs. negative) and arousal (mild vs. strong). Pupil dilation was highly sensitive to the valence of facial expressions, being larger for negative vs. positive facial expressions. A separate control experiment with luminance-matched non-face stimuli indicated that the valence effect was specific to facial expressions and cannot be explained by luminance confounds. Pupil response was not sensitive to the arousal level of facial expressions. The results show the feasibility of using pupil diameter as a marker of mothers' affective responses to ecologically valid infant stimuli and point to a particularly prompt maternal response to infant distress cues.

MiRNA Analysis by Quantitative PCR in Preterm Human Breast Milk Reveals Daily Fluctuations of hsa-miR-16-5p

PubMed Central

Floris, Ilaria; Billard, Hélène; Boquien, Clair-Yves; Joram-Gauvard, Evelyne; Simon, Laure; Legrand, Arnaud; Boscher, Cécile; Rozé, Jean-Christophe; Bolaños-Jiménez, Francisco; Kaeffer, Bertrand

2015-01-01

Background and Aims Human breast milk is an extremely dynamic fluid containing many biologically-active components which change throughout the feeding period and throughout the day. We designed a miRNA assay on minimized amounts of raw milk obtained from mothers of preterm infants. We investigated changes in miRNA expression within month 2 of lactation and then over the course of 24 hours. Materials and Methods Analyses were performed on pooled breast milk, made by combining samples collected at different clock times from the same mother donor, along with time series collected over 24 hours from four unsynchronized mothers. Whole milk, lipids or skim milk fractions were processed and analyzed by qPCR. We measured hsa-miR-16-5p, hsa-miR-21-5p, hsa-miR-146-5p, and hsa-let-7a, d and g (all -5p). Stability of miRNA endogenous controls was evaluated using RefFinder, a web tool integrating geNorm, Normfinder, BestKeeper and the comparative ΔΔCt method. Results MiR-21 and miR-16 were stably expressed in whole milk collected within month 2 of lactation from four mothers. Analysis of lipids and skim milk revealed that miR-146b and let-7d were better references in both fractions. Time series (5H-23H) allowed the identification of a set of three endogenous reference genes (hsa-let-7d, hsa-let-7g and miR-146b) to normalize raw quantification cycle (Cq) data. We identified a daily oscillation of miR-16-5p. Perspectives Our assay allows exploring miRNA levels of breast milk from mother with preterm baby collected in time series over 48–72 hours. PMID:26474056

Pretreatment microRNA Expression Impacting on Epithelial-to-Mesenchymal Transition Predicts Intrinsic Radiosensitivity in Head and Neck Cancer Cell Lines and Patients.

PubMed

de Jong, Monique C; Ten Hoeve, Jelle J; Grénman, Reidar; Wessels, Lodewyk F; Kerkhoven, Ron; Te Riele, Hein; van den Brekel, Michiel W M; Verheij, Marcel; Begg, Adrian C

2015-12-15

Predominant causes of head and neck cancer recurrence after radiotherapy are rapid repopulation, hypoxia, fraction of cancer stem cells, and intrinsic radioresistance. Currently, intrinsic radioresistance can only be assessed by ex vivo colony assays. Besides being time-consuming, colony assays do not identify causes of intrinsic resistance. We aimed to identify a biomarker for intrinsic radioresistance to be used before start of treatment and to reveal biologic processes that could be targeted to overcome intrinsic resistance. We analyzed both microRNA and mRNA expression in a large panel of head and neck squamous cell carcinoma (HNSCC) cell lines. Expression was measured on both irradiated and unirradiated samples. Results were validated using modified cell lines and a series of patients with laryngeal cancer. miRs, mRNAs, and gene sets that correlated with resistance could be identified from expression data of unirradiated cells. The presence of epithelial-to-mesenchymal transition (EMT) and low expression of miRs involved in the inhibition of EMT were important radioresistance determinants. This finding was validated in two independent cell line pairs, in which the induction of EMT reduced radiosensitivity. Moreover, low expression of the most important miR (miR-203) was shown to correlate with local disease recurrence after radiotherapy in a series of patients with laryngeal cancer. These findings indicate that EMT and low expression of EMT-inhibiting miRs, especially miR-203, measured in pretreatment material, causes intrinsic radioresistance of HNSCC, which could enable identification and treatment modification of radioresistant tumors. Clin Cancer Res; 21(24); 5630-8. ©2015 AACR. ©2015 American Association for Cancer Research.

The impact of seasonal signals on spatio-temporal filtering

NASA Astrophysics Data System (ADS)

Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz

2016-04-01

Existence of Common Mode Errors (CMEs) in permanent GNSS networks contribute to spatial and temporal correlation in residual time series. Time series from permanently observing GNSS stations of distance less than 2 000 km are similarly influenced by such CME sources as: mismodelling (Earth Orientation Parameters - EOP, satellite orbits or antenna phase center variations) during the process of the reference frame realization, large-scale atmospheric and hydrospheric effects as well as small scale crust deformations. Residuals obtained as a result of detrending and deseasonalising of topocentric GNSS time series arranged epoch-by-epoch form an observation matrix independently for each component (North, East, Up). CME is treated as internal structure of the data. Assuming a uniform temporal function across the network it is possible to filter CME out using PCA (Principal Component Analysis) approach. Some of above described CME sources may be reflected as a wide range of frequencies in GPS residual time series. In order to determine an impact of seasonal signals modeling to existence of spatial correlation in network and consequently the results of CME filtration, we chose two ways of modeling. The first approach was commonly presented by previous authors, who modeled with the Least-Squares Estimation (LSE) only annual and semi-annual oscillations. In the second one the set of residuals was a result of modeling of deterministic part that included fortnightly periods plus up to 9th harmonics of Chandlerian, tropical and draconitic oscillations. Correlation coefficients for residuals in parallel with KMO (Kaiser-Meyer-Olkin) statistic and Bartlett's test of sphericity were determined. For this research we used time series expressed in ITRF2008 provided by JPL (Jet Propulsion Laboratory). GPS processing was made using GIPSY-OASIS software in a PPP (Precise Point Positioning) mode. In order to form GPS station network that meet demands of uniform spatial response to the CME we chose 18 stations located in Central Europe. Created network extends up to 1500 kilometers. The KMO statistic indicate whether a component analysis may be useful for a chosen data set. We obtained KMO statistic value of 0.87 and 0.62 for residuals of Up component after first and second approaches were applied, what means that both residuals share common errors. Bartlett's test of sphericity analysis met a requirement that in both cases there are correlations in residuals. Another important results are the eigenvalues expressed as a percentage of the total variance explained by the first few components in PCA. For North, East and Up component we obtain respectively 68%, 75%, 65% and 47%, 54%, 52% after first and second approaches were applied. The results of CME filtration using PCA approach performed on both residual time series influence directly the uncertainty of the velocity of permanent stations. In our case spatial filtering reduces the uncertainty of velocity from 0.5 to 0.8 mm for horizontal components and from 0.6 to 0.9 mm on average for Up component when annual and semi-annual signals were assumed. Nevertheless, while second approach to the deterministic part modelling was used, deterioration of velocity uncertainty was noticed only for Up component, probably due to much higher autocorrelation in the time series when comparing to horizontal components.

Validation of Reference Genes for Gene Expression by Quantitative Real-Time RT-PCR in Stem Segments Spanning Primary to Secondary Growth in Populus tomentosa.

PubMed

Wang, Ying; Chen, Yajuan; Ding, Liping; Zhang, Jiewei; Wei, Jianhua; Wang, Hongzhi

2016-01-01

The vertical segments of Populus stems are an ideal experimental system for analyzing the gene expression patterns involved in primary and secondary growth during wood formation. Suitable internal control genes are indispensable to quantitative real time PCR (qRT-PCR) assays of gene expression. In this study, the expression stability of eight candidate reference genes was evaluated in a series of vertical stem segments of Populus tomentosa. Analysis through software packages geNorm, NormFinder and BestKeeper showed that genes ribosomal protein (RP) and tubulin beta (TUBB) were the most unstable across the developmental stages of P. tomentosa stems, and the combination of the three reference genes, eukaryotic translation initiation factor 5A (eIF5A), Actin (ACT6) and elongation factor 1-beta (EF1-beta) can provide accurate and reliable normalization of qRT-PCR analysis for target gene expression in stem segments undergoing primary and secondary growth in P. tomentosa. These results provide crucial information for transcriptional analysis in the P. tomentosa stem, which may help to improve the quality of gene expression data in these vertical stem segments, which constitute an excellent plant system for the study of wood formation.

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

PubMed Central

Hejblum, Boris P.; Skinner, Jason; Thiébaut, Rodolphe

2015-01-01

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package. PMID:26111374

Selection of internal control genes for quantitative real-time RT-PCR studies during tomato development process

PubMed Central

Expósito-Rodríguez, Marino; Borges, Andrés A; Borges-Pérez, Andrés; Pérez, José A

2008-01-01

Background The elucidation of gene expression patterns leads to a better understanding of biological processes. Real-time quantitative RT-PCR has become the standard method for in-depth studies of gene expression. A biologically meaningful reporting of target mRNA quantities requires accurate and reliable normalization in order to identify real gene-specific variation. The purpose of normalization is to control several variables such as different amounts and quality of starting material, variable enzymatic efficiencies of retrotranscription from RNA to cDNA, or differences between tissues or cells in overall transcriptional activity. The validity of a housekeeping gene as endogenous control relies on the stability of its expression level across the sample panel being analysed. In the present report we describe the first systematic evaluation of potential internal controls during tomato development process to identify which are the most reliable for transcript quantification by real-time RT-PCR. Results In this study, we assess the expression stability of 7 traditional and 4 novel housekeeping genes in a set of 27 samples representing different tissues and organs of tomato plants at different developmental stages. First, we designed, tested and optimized amplification primers for real-time RT-PCR. Then, expression data from each candidate gene were evaluated with three complementary approaches based on different statistical procedures. Our analysis suggests that SGN-U314153 (CAC), SGN-U321250 (TIP41), SGN-U346908 ("Expressed") and SGN-U316474 (SAND) genes provide superior transcript normalization in tomato development studies. We recommend different combinations of these exceptionally stable housekeeping genes for suited normalization of different developmental series, including the complete tomato development process. Conclusion This work constitutes the first effort for the selection of optimal endogenous controls for quantitative real-time RT-PCR studies of gene expression during tomato development process. From our study a tool-kit of control genes emerges that outperform the traditional genes in terms of expression stability. PMID:19102748

Modeling T-cell activation using gene expression profiling and state-space models.

PubMed

Rangel, Claudia; Angus, John; Ghahramani, Zoubin; Lioumi, Maria; Sotheran, Elizabeth; Gaiba, Alessia; Wild, David L; Falciani, Francesco

2004-06-12

We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses. Supplementary data and Matlab computer source code will be made available on the web at the URL given below. http://public.kgi.edu/~wild/LDS/index.htm

Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.

PubMed

Leifeld, Thomas; Zhang, Zhihua; Zhang, Ping

2018-01-01

Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge. Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response. Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.

Synchronizable Series Expressions. Part 2. Overview of the Theory and Implementation.

DTIC Science & Technology

1987-11-01

more running time than shown in the table. because time is eventually required in order to collect the garbage it creates. Program Running ’rime Garbage...possible to simply put an enumerator where it is used.) (loop for x integer from I to 4 collect x) - (lotS* ((x (Eup I :to 4))) (declare (type integer x...below. (loop for x from 1 to 4 and for y = 0 then (1- x) collect (list x y)) - (lotS* ((x (Eup 1 :to 4)) (y (Tprevious (1- x) 0))) (Rlist (list x y

Sampling rare fluctuations of discrete-time Markov chains

NASA Astrophysics Data System (ADS)

Whitelam, Stephen

2018-03-01

We describe a simple method that can be used to sample the rare fluctuations of discrete-time Markov chains. We focus on the case of Markov chains with well-defined steady-state measures, and derive expressions for the large-deviation rate functions (and upper bounds on such functions) for dynamical quantities extensive in the length of the Markov chain. We illustrate the method using a series of simple examples, and use it to study the fluctuations of a lattice-based model of active matter that can undergo motility-induced phase separation.

Sampling rare fluctuations of discrete-time Markov chains.

PubMed

Whitelam, Stephen

2018-03-01

We describe a simple method that can be used to sample the rare fluctuations of discrete-time Markov chains. We focus on the case of Markov chains with well-defined steady-state measures, and derive expressions for the large-deviation rate functions (and upper bounds on such functions) for dynamical quantities extensive in the length of the Markov chain. We illustrate the method using a series of simple examples, and use it to study the fluctuations of a lattice-based model of active matter that can undergo motility-induced phase separation.

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

NASA Astrophysics Data System (ADS)

Fatichi, S.; Ivanov, V. Y.; Caporali, E.

2013-04-01

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000-2009, 2046-2065 and 2081-2100, using the period of 1962-1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000-2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference

PubMed Central

Campbell, Kieran R.

2016-01-01

Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a ‘pseudotime’ where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference. PMID:27870852

TMV-Gate vectors: Gateway compatible tobacco mosaic virus based expression vectors for functional analysis of proteins

PubMed Central

Kagale, Sateesh; Uzuhashi, Shihomi; Wigness, Merek; Bender, Tricia; Yang, Wen; Borhan, M. Hossein; Rozwadowski, Kevin

2012-01-01

Plant viral expression vectors are advantageous for high-throughput functional characterization studies of genes due to their capability for rapid, high-level transient expression of proteins. We have constructed a series of tobacco mosaic virus (TMV) based vectors that are compatible with Gateway technology to enable rapid assembly of expression constructs and exploitation of ORFeome collections. In addition to the potential of producing recombinant protein at grams per kilogram FW of leaf tissue, these vectors facilitate either N- or C-terminal fusions to a broad series of epitope tag(s) and fluorescent proteins. We demonstrate the utility of these vectors in affinity purification, immunodetection and subcellular localisation studies. We also apply the vectors to characterize protein-protein interactions and demonstrate their utility in screening plant pathogen effectors. Given its broad utility in defining protein properties, this vector series will serve as a useful resource to expedite gene characterization efforts. PMID:23166857

Subtractive cloning of cDNA from Aspergillus oryzae differentially regulated between solid-state culture and liquid (submerged) culture.

PubMed

Akao, Takeshi; Gomi, Katsuya; Goto, Kuniyasu; Okazaki, Naoto; Akita, Osamu

2002-07-01

In solid-state cultures (SC), Aspergillus oryzae shows characteristics such as high-level production and secretion of enzymes and hyphal differentiation with asexual development which are absent in liquid (submerged) culture (LC). It was predicted that many of the genes involved in the characteristics of A. oryzae in SC are differentially expressed between SC and LC. We generated two subtracted cDNA libraries with bi-directional cDNA subtractive hybridizations to isolate and identify such genes. Among them, we identified genes upregulated in or specific to SC, such as the AOS ( A. oryzae SC-specific gene) series, and those downregulated or not expressed in SC, such as the AOL ( A. oryzae LC-specific) series. Sequencing analyses revealed that the AOS series and the AOL series contain genes encoding extra- and intracellular enzymes and transport proteins. However, half were functionally unclassified by nucleotide sequences. Also, by expression profile, the AOS series comprised two groups. These gene products' molecular functions and physiological roles in SC await further investigation.

Gene coexpression measures in large heterogeneous samples using count statistics.

PubMed

Wang, Y X Rachel; Waterman, Michael S; Huang, Haiyan

2014-11-18

With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology has been an important part of the "big data" challenge. Gene coexpression is one of the earliest techniques developed that is still widely in use for functional annotation, pathway analysis, and, most importantly, the reconstruction of gene regulatory networks, based on gene expression data. However, most coexpression measures do not specifically account for local features in expression profiles. For example, it is very likely that the patterns of gene association may change or only exist in a subset of the samples, especially when the samples are pooled from a range of experiments. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.

Emotion and food. Do the emotions expressed on other people's faces affect the desire to eat liked and disliked food products?

PubMed

Barthomeuf, L; Rousset, S; Droit-Volet, S

2009-02-01

The aim of the present study was to test if pleasure, neutrality and disgust expressed by other individuals on a photograph could affect the desire to eat liked or disliked food products. Forty-four men and women were presented with two series of photographs. The first series of photographs was composed of six food photographs: three liked and three disliked food products. The second series consisted of the same photographs presented with eaters expressing three different emotions: disgust, pleasure or neutrality. Results showed that the effect of the presence of an eater, and of emotions expressed by this eater, depended on the food category. For the liked foods, the desire to eat was higher when these foods were presented alone than with an eater expressing neutral emotion. When the eater expressed pleasure, the desire to eat these liked foods did not significantly increase. In contrast, when the eater expressed disgust, the desire to eat them significantly decreased. When the foods were disliked, the influence of the pleasant social context was stronger than for the liked foods. The desire to eat the disliked foods actually increased in the presence of an eater expressing pleasure. On the contrary, the disgust and neutral context had no effect on the desire for disliked foods.

Leverage effect and its causality in the Korea composite stock price index

NASA Astrophysics Data System (ADS)

Lee, Chang-Yong

2012-02-01

In this paper, we investigate the leverage effect and its causality in the time series of the Korea Composite Stock Price Index from November of 1997 to September of 2010. The leverage effect, which can be quantitatively expressed as a negative correlation between past return and future volatility, is measured by using the cross-correlation coefficient of different time lags between the two time series of the return and the volatility. We find that past return and future volatility are negatively correlated and that the cross correlation is moderate and decays over 60 trading days. We also carry out a partial correlation analysis in order to confirm that the negative correlation between past return and future volatility is neither an artifact nor influenced by the traded volume. To determine the causality of the leverage effect within the decay time, we additionally estimate the cross correlation between past volatility and future return. With the estimate, we perform a statistical hypothesis test to demonstrate that the causal relation is in favor of the return influencing the volatility rather than the other way around.

Separation of components from a scale mixture of Gaussian white noises

NASA Astrophysics Data System (ADS)

Vamoş, Călin; Crăciun, Maria

2010-05-01

The time evolution of a physical quantity associated with a thermodynamic system whose equilibrium fluctuations are modulated in amplitude by a slowly varying phenomenon can be modeled as the product of a Gaussian white noise {Zt} and a stochastic process with strictly positive values {Vt} referred to as volatility. The probability density function (pdf) of the process Xt=VtZt is a scale mixture of Gaussian white noises expressed as a time average of Gaussian distributions weighted by the pdf of the volatility. The separation of the two components of {Xt} can be achieved by imposing the condition that the absolute values of the estimated white noise be uncorrelated. We apply this method to the time series of the returns of the daily S&P500 index, which has also been analyzed by means of the superstatistics method that imposes the condition that the estimated white noise be Gaussian. The advantage of our method is that this financial time series is processed without partitioning or removal of the extreme events and the estimated white noise becomes almost Gaussian only as result of the uncorrelation condition.

A technique to detect microclimatic inhomogeneities in historical temperature records

NASA Astrophysics Data System (ADS)

Runnalls, K. E.; Oke, T. R.

2003-04-01

A technique to identify inhomogeneities in historical temperature records caused by microclimatic changes to the surroundings of a climate station (e.g. minor instrument relocations, vegetation growth/removal, construction of houses, roads, runways) is presented. The technique uses daily maximum and minimum temperatures to estimate the magnitude of nocturnal cooling. The test station is compared to a nearby reference station by constructing time series of monthly "cooling ratios". It is argued that the cooling ratio is a particularly sensitive measure of microclimatic differences between neighbouring climate stations. Firstly, because microclimatic character is best expressed at night in stable conditions. Secondly, because larger-scale climatic influences common to both stations are removed by the use of a ratio and, because the ratio can be shown to be invariant in the mean with weather variables such as wind and cloud. Inflections (change points) in time series of cooling ratios therefore signal microclimatic change in one of the station records. Hurst rescaling is applied to the time series to aid in the identification of change points, which can then be compared to documented station history events, if sufficient metatdata is available. Results for a variety of air temperature records, ranging from rural to urban stations, are presented to illustrate the applicability of the technique.

Interpretation of a compositional time series

NASA Astrophysics Data System (ADS)

Tolosana-Delgado, R.; van den Boogaart, K. G.

2012-04-01

Common methods for multivariate time series analysis use linear operations, from the definition of a time-lagged covariance/correlation to the prediction of new outcomes. However, when the time series response is a composition (a vector of positive components showing the relative importance of a set of parts in a total, like percentages and proportions), then linear operations are afflicted of several problems. For instance, it has been long recognised that (auto/cross-)correlations between raw percentages are spurious, more dependent on which other components are being considered than on any natural link between the components of interest. Also, a long-term forecast of a composition in models with a linear trend will ultimately predict negative components. In general terms, compositional data should not be treated in a raw scale, but after a log-ratio transformation (Aitchison, 1986: The statistical analysis of compositional data. Chapman and Hill). This is so because the information conveyed by a compositional data is relative, as stated in their definition. The principle of working in coordinates allows to apply any sort of multivariate analysis to a log-ratio transformed composition, as long as this transformation is invertible. This principle is of full application to time series analysis. We will discuss how results (both auto/cross-correlation functions and predictions) can be back-transformed, viewed and interpreted in a meaningful way. One view is to use the exhaustive set of all possible pairwise log-ratios, which allows to express the results into D(D - 1)/2 separate, interpretable sets of one-dimensional models showing the behaviour of each possible pairwise log-ratios. Another view is the interpretation of estimated coefficients or correlations back-transformed in terms of compositions. These two views are compatible and complementary. These issues are illustrated with time series of seasonal precipitation patterns at different rain gauges of the USA. In this data set, the proportion of annual precipitation falling in winter, spring, summer and autumn is considered a 4-component time series. Three invertible log-ratios are defined for calculations, balancing rainfall in autumn vs. winter, in summer vs. spring, and in autumn-winter vs. spring-summer. Results suggest a 2-year correlation range, and certain oscillatory behaviour in the last balance, which does not occur in the other two.

Predicting earthquakes by analyzing accelerating precursory seismic activity

USGS Publications Warehouse

Varnes, D.J.

1989-01-01

During 11 sequences of earthquakes that in retrospect can be classed as foreshocks, the accelerating rate at which seismic moment is released follows, at least in part, a simple equation. This equation (1) is {Mathematical expression},where {Mathematical expression} is the cumulative sum until time, t, of the square roots of seismic moments of individual foreshocks computed from reported magnitudes;C and n are constants; and tfis a limiting time at which the rate of seismic moment accumulation becomes infinite. The possible time of a major foreshock or main shock, tf,is found by the best fit of equation (1), or its integral, to step-like plots of {Mathematical expression} versus time using successive estimates of tfin linearized regressions until the maximum coefficient of determination, r2,is obtained. Analyzed examples include sequences preceding earthquakes at Cremasta, Greece, 2/5/66; Haicheng, China 2/4/75; Oaxaca, Mexico, 11/29/78; Petatlan, Mexico, 3/14/79; and Central Chile, 3/3/85. In 29 estimates of main-shock time, made as the sequences developed, the errors in 20 were less than one-half and in 9 less than one tenth the time remaining between the time of the last data used and the main shock. Some precursory sequences, or parts of them, yield no solution. Two sequences appear to include in their first parts the aftershocks of a previous event; plots using the integral of equation (1) show that the sequences are easily separable into aftershock and foreshock segments. Synthetic seismic sequences of shocks at equal time intervals were constructed to follow equation (1), using four values of n. In each series the resulting distributions of magnitudes closely follow the linear Gutenberg-Richter relation log N=a-bM, and the product n times b for each series is the same constant. In various forms and for decades, equation (1) has been used successfully to predict failure times of stressed metals and ceramics, landslides in soil and rock slopes, and volcanic eruptions. Results of more recent experiments and theoretical studies on crack propagation, fault mechanics, and acoustic emission can be closely reproduced by equation (1). Rate-process theory and continuum damage mechanics offer leads toward understanding the physical processes. ?? 1989 Birkha??user Verlag.

Additional in-series compliance reduces muscle force summation and alters the time course of force relaxation during fixed-end contractions.

PubMed

Mayfield, Dean L; Launikonis, Bradley S; Cresswell, Andrew G; Lichtwark, Glen A

2016-11-15

There are high mechanical demands placed on skeletal muscles in movements requiring rapid acceleration of the body or its limbs. Tendons are responsible for transmitting muscle forces, but, because of their elasticity, can manipulate the mechanics of the internal contractile apparatus. Shortening of the contractile apparatus against the stretch of tendon affects force generation according to known mechanical properties; however, the extent to which differences in tendon compliance alter force development in response to a burst of electrical impulses is unclear. To establish the influence of series compliance on force summation, we studied electrically evoked doublet contractions in the cane toad peroneus muscle in the presence and absence of a compliant artificial tendon. Additional series compliance reduced tetanic force by two-thirds, a finding predicted based on the force-length property of skeletal muscle. Doublet force and force-time integral expressed relative to the twitch were also reduced by additional series compliance. Active shortening over a larger range of the ascending limb of the force-length curve and at a higher velocity, leading to a progressive reduction in force-generating potential, could be responsible. Muscle-tendon interaction may also explain the accelerated time course of force relaxation in the presence of additional compliance. Our findings suggest that a compliant tendon limits force summation under constant-length conditions. However, high series compliance can be mechanically advantageous when a muscle-tendon unit is actively stretched, permitting muscle fibres to generate force almost isometrically, as shown during stretch-shorten cycles in locomotor activities. Restricting active shortening would likely favour rapid force development. © 2016. Published by The Company of Biologists Ltd.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olivier, C. P., E-mail: colivier@sansa.org.za; Maharaj, S. K., E-mail: smaharaj@sansa.org.za, E-mail: rbharuthram@uwc.ac.za; Bharuthram, R., E-mail: rbharuthram@uwc.ac.za

In a series of papers by Maharaj et al., including “Existence domains of slow and fast ion-acoustic solitons in two-ion space plasmas” [Phys. Plasmas 22, 032313 (2015)], incorrect expressions for the Sagdeev potential are presented. In this paper, we provide the correct expression of the Sagdeev potential. The correct expression was used to generate the numerical results for the above-mentioned series of papers, so that all results and conclusions are correct, despite the wrong Sagdeev potential expressions printed in the papers. The correct expression of the Sagdeev potential presented here is a very useful generic expression in the sense thatmore » a single expression can be used to study nonlinear structures associated with any acoustic mode, despite the fact that the supersonic and subsonic species would vary if solitons associated with different linear modes are studied.« less

Enhancement of creative expression and entoptic phenomena as after-effects of repeated ayahuasca ceremonies.

PubMed

Frecska, Ede; Móré, Csaba E; Vargha, András; Luna, Luis E

2012-01-01

Studying the effect of psychedelic substances on expression of creativity is a challenging problem. Our primary objective was to study the psychometric measures of creativity after a series of ayahuasca ceremonies at a time when the acute effects have subsided. The secondary objective was to investigate how entoptic phenomena emerge during expression of creativity. Forty individuals who were self-motivated participants of ayahuasca rituals in Brazil completed the visual components of the Torrance Tests of Creative Thinking before and the second day after the end of a two-week long ceremony series. Twenty-one comparison subjects who did not participate in recent psychedelic use also took the Torrance tests twice, two weeks apart. Repeated ingestion of ayahuasca in the ritual setting significantly increased the number of highly original solutions and phosphenic responses. However, participants in the ayahuasca ceremonies exhibited more phosphenic solutions already at the baseline, probably due to the fact that they had more psychedelic experiences within six months prior to the study than the comparison subjects did. This naturalistic study supports the notion that some measures of visual creativity may increase after ritual use of ayahuasca, when the acute psychoactive effects are receded. It also demonstrates an increased entoptic activity after repeated ayahuasca ingestion.

Identification of stress responsive genes by studying specific relationships between mRNA and protein abundance.

PubMed

Morimoto, Shimpei; Yahara, Koji

2018-03-01

Protein expression is regulated by the production and degradation of mRNAs and proteins but the specifics of their relationship are controversial. Although technological advances have enabled genome-wide and time-series surveys of mRNA and protein abundance, recent studies have shown paradoxical results, with most statistical analyses being limited to linear correlation, or analysis of variance applied separately to mRNA and protein datasets. Here, using recently analyzed genome-wide time-series data, we have developed a statistical analysis framework for identifying which types of genes or biological gene groups have significant correlation between mRNA and protein abundance after accounting for potential time delays. Our framework stratifies all genes in terms of the extent of time delay, conducts gene clustering in each stratum, and performs a non-parametric statistical test of the correlation between mRNA and protein abundance in a gene cluster. Consequently, we revealed stronger correlations than previously reported between mRNA and protein abundance in two metabolic pathways. Moreover, we identified a pair of stress responsive genes ( ADC17 and KIN1 ) that showed a highly similar time series of mRNA and protein abundance. Furthermore, we confirmed robustness of the analysis framework by applying it to another genome-wide time-series data and identifying a cytoskeleton-related gene cluster (keratin 18, keratin 17, and mitotic spindle positioning) that shows similar correlation. The significant correlation and highly similar changes of mRNA and protein abundance suggests a concerted role of these genes in cellular stress response, which we consider provides an answer to the question of the specific relationships between mRNA and protein in a cell. In addition, our framework for studying the relationship between mRNAs and proteins in a cell will provide a basis for studying specific relationships between mRNA and protein abundance after accounting for potential time delays.

Dollar$ & $en$e. Part IV: Measuring the value of people, structural, and customer capital.

PubMed

Wilkinson, I

2001-01-01

In Part I of this series, I introduced the concept of memes (1). Memes are ideas or concepts, the information world equivalent of genes. The goal of this series of articles is to infect you with my memes, so that you will assimilate, translate, and express them. We discovered that no matter what our area of expertise or "-ology," we all are in the information business. Our goal is to be in the wisdom business. We saw that when we convert raw data into wisdom we are moving along a value chain. Each step in the chain adds a different amount of value to the final product: timely, relevant, accurate, and precise knowledge which can then be applied to create the ultimate product in the value chain: wisdom. In Part II of this series, I infected you with a set of memes for measuring the cost of adding value (2). In Part III of this series, I infected you with a new set of memes for measuring the added value of knowledge, i.e., intellectual capital (3). In Part IV of this series, I will infect you with memes for measuring the value of people, structural, and customer capital.

Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes.

PubMed

Brock, Guy N; Shaffer, John R; Blakesley, Richard E; Lotz, Meredith J; Tseng, George C

2008-01-10

Gene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures x time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set. We found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS) scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS) scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost. Our findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA) are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA) performed better on mcroarray data with lower complexity, while neighbour-based methods (KNN, OLS, LSA, LLS) performed better in data with higher complexity. We also found that the EBS and STS schemes serve as complementary and effective tools for selecting the optimal imputation algorithm.

Approximate Expressions for the Period of a Simple Pendulum Using a Taylor Series Expansion

ERIC Educational Resources Information Center

Belendez, Augusto; Arribas, Enrique; Marquez, Andres; Ortuno, Manuel; Gallego, Sergi

2011-01-01

An approximate scheme for obtaining the period of a simple pendulum for large-amplitude oscillations is analysed and discussed. When students express the exact frequency or the period of a simple pendulum as a function of the oscillation amplitude, and they are told to expand this function in a Taylor series, they always do so using the…

Multiscale characterization and prediction of monsoon rainfall in India using Hilbert-Huang transform and time-dependent intrinsic correlation analysis

NASA Astrophysics Data System (ADS)

Adarsh, S.; Reddy, M. Janga

2017-07-01

In this paper, the Hilbert-Huang transform (HHT) approach is used for the multiscale characterization of All India Summer Monsoon Rainfall (AISMR) time series and monsoon rainfall time series from five homogeneous regions in India. The study employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for multiscale decomposition of monsoon rainfall in India and uses the Normalized Hilbert Transform and Direct Quadrature (NHT-DQ) scheme for the time-frequency characterization. The cross-correlation analysis between orthogonal modes of All India monthly monsoon rainfall time series and that of five climate indices such as Quasi Biennial Oscillation (QBO), El Niño Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multi Decadal Oscillation (AMO), and Equatorial Indian Ocean Oscillation (EQUINOO) in the time domain showed that the links of different climate indices with monsoon rainfall are expressed well only for few low-frequency modes and for the trend component. Furthermore, this paper investigated the hydro-climatic teleconnection of ISMR in multiple time scales using the HHT-based running correlation analysis technique called time-dependent intrinsic correlation (TDIC). The results showed that both the strength and nature of association between different climate indices and ISMR vary with time scale. Stemming from this finding, a methodology employing Multivariate extension of EMD and Stepwise Linear Regression (MEMD-SLR) is proposed for prediction of monsoon rainfall in India. The proposed MEMD-SLR method clearly exhibited superior performance over the IMD operational forecast, M5 Model Tree (MT), and multiple linear regression methods in ISMR predictions and displayed excellent predictive skill during 1989-2012 including the four extreme events that have occurred during this period.

Detecting cells in time varying intensity images in confocal microscopy for gene expression studies in living cells

NASA Astrophysics Data System (ADS)

Mitra, Debasis; Boutchko, Rostyslav; Ray, Judhajeet; Nilsen-Hamilton, Marit

2015-03-01

In this work we present a time-lapsed confocal microscopy image analysis technique for an automated gene expression study of multiple single living cells. Fluorescence Resonance Energy Transfer (FRET) is a technology by which molecule-to-molecule interactions are visualized. We analyzed a dynamic series of ~102 images obtained using confocal microscopy of fluorescence in yeast cells containing RNA reporters that give a FRET signal when the gene promoter is activated. For each time frame, separate images are available for three spectral channels and the integrated intensity snapshot of the system. A large number of time-lapsed frames must be analyzed to identify each cell individually across time and space, as it is moving in and out of the focal plane of the microscope. This makes it a difficult image processing problem. We have proposed an algorithm here, based on scale-space technique, which solves the problem satisfactorily. The algorithm has multiple directions for even further improvement. The ability to rapidly measure changes in gene expression simultaneously in many cells in a population will open the opportunity for real-time studies of the heterogeneity of genetic response in a living cell population and the interactions between cells that occur in a mixed population, such as the ones found in the organs and tissues of multicellular organisms.

Major and c-series gangliosides in lenticular tissues: mammals to molluscs.

PubMed

Saito, M; Sugiyama, K

2001-10-01

Gangliosides of eye lenses were examined in mammals (rat, rabbits, pig, cow), bird (chicken), reptile (terrapin), amphibian (bullfrog), bony fish (red sea bream, bluefin tuna, bonito, Pacific mackerel) and molluscs (common squid, Pacific octopus). Besides the fact that GM3 was the common ganglioside species, the composition of major gangliosides in mammalian eye lenses significantly differed from each other. While gangliotetraose gangliosides were abundant in rat eye lens, they did not constitute major components in porcine and bovine tissues. The c-series ganglioside GT3 was expressed in rat eye lenses but were practically absent in other mammalian tissues. The composition of major gangliosides in eye lenses of lower animals varied from species to species, whereas c-series gangliosides were consistently expressed, showing similar compositional profiles. Our results demonstrate the species-specific compositions of lenticular gangliosides. Evidence was also provided suggesting that eye lenses of common squid (Todarodes pacificus) and Pacific octopus (Octopus vulgaris) express gangliosides including gangliotetraose species and c-series gangliosides.

Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

PubMed Central

2011-01-01

Background Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study. Conclusions Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices. PMID:21771321

Enhanced Cell-Specific Ablation in Zebrafish Using a Triple Mutant of Escherichia Coli Nitroreductase

PubMed Central

Mathias, Jonathan R.; Zhang, Zhanying; Saxena, Meera T.

2014-01-01

Abstract Transgenic expression of bacterial nitroreductase (NTR) facilitates chemically-inducible targeted cell ablation. In zebrafish, the NTR system enables studies of cell function and cellular regeneration. Metronidazole (MTZ) has become the most commonly used prodrug substrate for eliciting cell loss in NTR-expressing transgenic zebrafish due to the cell-specific nature of its cytotoxic derivatives. Unfortunately, MTZ treatments required for effective cell ablation border toxic effects, and, thus, likely incur undesirable nonspecific effects. Here, we tested whether a triple mutant variant of NTR, previously shown to display improved activity in bacterial assays, can solve this issue by promoting cell ablation in zebrafish using reduced prodrug treatment regimens. We generated several complementary transgenic zebrafish lines expressing either wild-type or mutant NTR (mutNTR) in specific neural cell types, and assayed prodrug-induced cell ablation kinetics using confocal time series imaging and plate reader-based quantification of fluorescent reporters expressed in targeted cell types. The results show that cell ablation can be achieved in mutNTR expressing transgenic lines with markedly shortened prodrug exposure times and/or at lower prodrug concentrations. The mutNTR variant characterized here can circumvent problematic nonspecific/toxic effects arising from low prodrug conversion efficiency, thus increasing the effectiveness and versatility of this selective cell ablation methodology. PMID:24428354

Enhanced cell-specific ablation in zebrafish using a triple mutant of Escherichia coli nitroreductase.

PubMed

Mathias, Jonathan R; Zhang, Zhanying; Saxena, Meera T; Mumm, Jeff S

2014-04-01

Transgenic expression of bacterial nitroreductase (NTR) facilitates chemically-inducible targeted cell ablation. In zebrafish, the NTR system enables studies of cell function and cellular regeneration. Metronidazole (MTZ) has become the most commonly used prodrug substrate for eliciting cell loss in NTR-expressing transgenic zebrafish due to the cell-specific nature of its cytotoxic derivatives. Unfortunately, MTZ treatments required for effective cell ablation border toxic effects, and, thus, likely incur undesirable nonspecific effects. Here, we tested whether a triple mutant variant of NTR, previously shown to display improved activity in bacterial assays, can solve this issue by promoting cell ablation in zebrafish using reduced prodrug treatment regimens. We generated several complementary transgenic zebrafish lines expressing either wild-type or mutant NTR (mutNTR) in specific neural cell types, and assayed prodrug-induced cell ablation kinetics using confocal time series imaging and plate reader-based quantification of fluorescent reporters expressed in targeted cell types. The results show that cell ablation can be achieved in mutNTR expressing transgenic lines with markedly shortened prodrug exposure times and/or at lower prodrug concentrations. The mutNTR variant characterized here can circumvent problematic nonspecific/toxic effects arising from low prodrug conversion efficiency, thus increasing the effectiveness and versatility of this selective cell ablation methodology.

Influence of the Tumor Microenvironment on Genomic Changes Conferring Chemoresistance in Breast Cancer

DTIC Science & Technology

2013-04-01

tumor microenvironment on clonal selection using intravital microscopy Jae-Hyun Park 1 , Miriam R. Fein 1 , Mikala Egeblad 1 1 Cold Spring Harbor...used surgically implanted mammary imaging windows in immunocompetent mice and injected “brainbow” expressing, syngeneic 4T1 breast carcinoma cells...under the windows. This allowed us to acquire multiple time- lapse imaging series by spinning disk confocal microscopy of the same tumor, done about 3

COMMENTS AND OPINIONS OF STUDENTS AT ABINGTON HIGH SCHOOL NORTH CAMPUS CONCERNING THE LIBRARY, A REPORT ON THE RESULTS OF A STUDENT OPINIONNAIRE.

ERIC Educational Resources Information Center

LUECKE, FRITZ; SPROESSER, GERRY

A RANDOMLY SELECTED SAMPLE OF 163 NINTH AND TENTH GRADE STUDENTS WAS ASKED, IN A SERIES OF QUESTIONS, TO EXPRESS THEIR ATTITUDE TOWARD THE SCHOOL LIBRARY. THE RESPONSES TO EACH STATEMENT WERE TALLIED AND PRESENTED AS A NUMBER AND PERCENTAGE. IN GENERAL IT WAS FOUND THAT THE MAJORITY OF THE STUDENTS SPEND SOME OF THEIR INDEPENDENT STUDY TIME IN THE…

Dollar$ & $en$e. Part V: What is your added value?

PubMed

Wilkinson, I

2001-01-01

In Part I of this series, I introduced the concept of memes (1). Memes are ideas or concepts--the information world equivalent of genes. The goal of this series of articles is to infect you with memes, so that you will assimilate, translate, and express them. No matter what our area of expertise or "-ology," we all are in the information business. Our goal is to be in the wisdom business. In the previous papers in this series, I showed that when we convert raw data into wisdom we are moving along a value chain. Each step in the chain adds a different amount of value to the final product: timely, relevant, accurate, and precise knowledge that can be applied to create the ultimate product in the value chain: wisdom. In Part II of this series, I introduced a set of memes for measuring the cost of adding value (2). In Part III of this series, I presented a new set of memes for measuring the added value of knowledge, i.e., intellectual capital (3). In Part IV of this series, I discussed practical knowledge management tools for measuring the value of people, structural, and customer capital (4). In Part V of this series, I will apply intellectual capital and knowledge management concepts at the individual level, to help answer a fundamental question: What is my added value?

Knockdown of long noncoding RNA 00152 (LINC00152) inhibits human retinoblastoma progression.

PubMed

Li, Songhe; Wen, Dacheng; Che, Songtian; Cui, Zhihua; Sun, Yabin; Ren, Hua; Hao, Jilong

2018-01-01

A growing body of evidence supports the involvement of long noncoding RNA 00152 (LINC00152) in the progression and metastasis of multiple cancers. However, the exact roles of LINC00152 in the progression of human retinoblastoma (RB) remain unknown. We explored the expression and biological function of human RB. The expression level of LINC00152 in RB tissues and cells was analyzed using quantitative real-time PCR. The function of LINC00152 was determined using a series of in vitro assays. In vivo, a nude mouse model was established to analyze the function of LINC00152. Gene and protein expressions were detected using quantitative real-time PCR and Western blot assays, respectively. The expression of LINC00152 mRNA was upregulated in RB tissues and cell lines. Knockdown of LINC00152 significantly inhibited cell proliferation, colony formation, migration, and invasion and promoted cell apoptosis and caspase-3 and caspase-8 activities in vitro, as well as suppressing tumorigenesis in vivo. We identified several genes related to proliferation, apoptosis, and invasion including Ki-67, Bcl-2, and MMP-9 that were transcriptionally inactivated by LINC00152. Taken together, these data implicate LINC00152 as a therapeutic target in RB.

Application of Gene Expression Trajectories Initiated from ErbB Receptor Activation Highlights the Dynamics of Divergent Promoter Usage.

PubMed

Carbajo, Daniel; Magi, Shigeyuki; Itoh, Masayoshi; Kawaji, Hideya; Lassmann, Timo; Arner, Erik; Forrest, Alistair R R; Carninci, Piero; Hayashizaki, Yoshihide; Daub, Carsten O; Okada-Hatakeyama, Mariko; Mar, Jessica C

2015-01-01

Understanding how cells use complex transcriptional programs to alter their fate in response to specific stimuli is an important question in biology. For the MCF-7 human breast cancer cell line, we applied gene expression trajectory models to identify the genes involved in driving cell fate transitions. We modified trajectory models to account for the scenario where cells were exposed to different stimuli, in this case epidermal growth factor and heregulin, to arrive at different cell fates, i.e. proliferation and differentiation respectively. Using genome-wide CAGE time series data collected from the FANTOM5 consortium, we identified the sets of promoters that were involved in the transition of MCF-7 cells to their specific fates versus those with expression changes that were generic to both stimuli. Of the 1,552 promoters identified, 1,091 had stimulus-specific expression while 461 promoters had generic expression profiles over the time course surveyed. Many of these stimulus-specific promoters mapped to key regulators of the ERK (extracellular signal-regulated kinases) signaling pathway such as FHL2 (four and a half LIM domains 2). We observed that in general, generic promoters peaked in their expression early on in the time course, while stimulus-specific promoters tended to show activation of their expression at a later stage. The genes that mapped to stimulus-specific promoters were enriched for pathways that control focal adhesion, p53 signaling and MAPK signaling while generic promoters were enriched for cell death, transcription and the cell cycle. We identified 162 genes that were controlled by an alternative promoter during the time course where a subset of 37 genes had separate promoters that were classified as stimulus-specific and generic. The results of our study highlighted the degree of complexity involved in regulating a cell fate transition where multiple promoters mapping to the same gene can demonstrate quite divergent expression profiles.

Arabidopsis Defense against Botrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic Analysis[C][W

PubMed Central

Windram, Oliver; Madhou, Priyadharshini; McHattie, Stuart; Hill, Claire; Hickman, Richard; Cooke, Emma; Jenkins, Dafyd J.; Penfold, Christopher A.; Baxter, Laura; Breeze, Emily; Kiddle, Steven J.; Rhodes, Johanna; Atwell, Susanna; Kliebenstein, Daniel J.; Kim, Youn-sung; Stegle, Oliver; Borgwardt, Karsten; Zhang, Cunjin; Tabrett, Alex; Legaie, Roxane; Moore, Jonathan; Finkenstadt, Bärbel; Wild, David L.; Mead, Andrew; Rand, David; Beynon, Jim; Ott, Sascha; Buchanan-Wollaston, Vicky; Denby, Katherine J.

2012-01-01

Transcriptional reprogramming forms a major part of a plant’s response to pathogen infection. Many individual components and pathways operating during plant defense have been identified, but our knowledge of how these different components interact is still rudimentary. We generated a high-resolution time series of gene expression profiles from a single Arabidopsis thaliana leaf during infection by the necrotrophic fungal pathogen Botrytis cinerea. Approximately one-third of the Arabidopsis genome is differentially expressed during the first 48 h after infection, with the majority of changes in gene expression occurring before significant lesion development. We used computational tools to obtain a detailed chronology of the defense response against B. cinerea, highlighting the times at which signaling and metabolic processes change, and identify transcription factor families operating at different times after infection. Motif enrichment and network inference predicted regulatory interactions, and testing of one such prediction identified a role for TGA3 in defense against necrotrophic pathogens. These data provide an unprecedented level of detail about transcriptional changes during a defense response and are suited to systems biology analyses to generate predictive models of the gene regulatory networks mediating the Arabidopsis response to B. cinerea. PMID:23023172

A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-11-01

Precipitation plays an important role in determining the climate of a region. Precise estimation of precipitation is required to manage and plan water resources, as well as other related applications such as hydrology, climatology, meteorology and agriculture. Time series of hydrologic variables such as precipitation are composed of deterministic and stochastic parts. Despite this fact, the stochastic part of the precipitation data is not usually considered in modeling of precipitation process. As an innovation, the present study introduces three new hybrid models by integrating soft computing methods including multivariate adaptive regression splines (MARS), Bayesian networks (BN) and gene expression programming (GEP) with a time series model, namely generalized autoregressive conditional heteroscedasticity (GARCH) for modeling of the monthly precipitation. For this purpose, the deterministic (obtained by soft computing methods) and stochastic (obtained by GARCH time series model) parts are combined with each other. To carry out this research, monthly precipitation data of Babolsar, Bandar Anzali, Gorgan, Ramsar, Tehran and Urmia stations with different climates in Iran were used during the period of 1965-2014. Root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE) and determination coefficient (R2) were employed to evaluate the performance of conventional/single MARS, BN and GEP, as well as the proposed MARS-GARCH, BN-GARCH and GEP-GARCH hybrid models. It was found that the proposed novel models are more precise than single MARS, BN and GEP models. Overall, MARS-GARCH and BN-GARCH models yielded better accuracy than GEP-GARCH. The results of the present study confirmed the suitability of proposed methodology for precise modeling of precipitation.

Correlations in magnitude series to assess nonlinearities: Application to multifractal models and heartbeat fluctuations.

PubMed

Bernaola-Galván, Pedro A; Gómez-Extremera, Manuel; Romance, A Ramón; Carpena, Pedro

2017-09-01

The correlation properties of the magnitude of a time series are associated with nonlinear and multifractal properties and have been applied in a great variety of fields. Here we have obtained the analytical expression of the autocorrelation of the magnitude series (C_{|x|}) of a linear Gaussian noise as a function of its autocorrelation (C_{x}). For both, models and natural signals, the deviation of C_{|x|} from its expectation in linear Gaussian noises can be used as an index of nonlinearity that can be applied to relatively short records and does not require the presence of scaling in the time series under study. In a model of artificial Gaussian multifractal signal we use this approach to analyze the relation between nonlinearity and multifractallity and show that the former implies the latter but the reverse is not true. We also apply this approach to analyze experimental data: heart-beat records during rest and moderate exercise. For each individual subject, we observe higher nonlinearities during rest. This behavior is also achieved on average for the analyzed set of 10 semiprofessional soccer players. This result agrees with the fact that other measures of complexity are dramatically reduced during exercise and can shed light on its relationship with the withdrawal of parasympathetic tone and/or the activation of sympathetic activity during physical activity.

Correlations in magnitude series to assess nonlinearities: Application to multifractal models and heartbeat fluctuations

NASA Astrophysics Data System (ADS)

Bernaola-Galván, Pedro A.; Gómez-Extremera, Manuel; Romance, A. Ramón; Carpena, Pedro

2017-09-01

The correlation properties of the magnitude of a time series are associated with nonlinear and multifractal properties and have been applied in a great variety of fields. Here we have obtained the analytical expression of the autocorrelation of the magnitude series (C|x |) of a linear Gaussian noise as a function of its autocorrelation (Cx). For both, models and natural signals, the deviation of C|x | from its expectation in linear Gaussian noises can be used as an index of nonlinearity that can be applied to relatively short records and does not require the presence of scaling in the time series under study. In a model of artificial Gaussian multifractal signal we use this approach to analyze the relation between nonlinearity and multifractallity and show that the former implies the latter but the reverse is not true. We also apply this approach to analyze experimental data: heart-beat records during rest and moderate exercise. For each individual subject, we observe higher nonlinearities during rest. This behavior is also achieved on average for the analyzed set of 10 semiprofessional soccer players. This result agrees with the fact that other measures of complexity are dramatically reduced during exercise and can shed light on its relationship with the withdrawal of parasympathetic tone and/or the activation of sympathetic activity during physical activity.

Rapid changes in gene expression direct rapid shifts in intestinal form and function in the Burmese python after feeding.

PubMed

Andrew, Audra L; Card, Daren C; Ruggiero, Robert P; Schield, Drew R; Adams, Richard H; Pollock, David D; Secor, Stephen M; Castoe, Todd A

2015-05-01

Snakes provide a unique and valuable model system for studying the extremes of physiological remodeling because of the ability of some species to rapidly upregulate organ form and function upon feeding. The predominant model species used to study such extreme responses has been the Burmese python because of the extreme nature of postfeeding response in this species. We analyzed the Burmese python intestine across a time series, before, during, and after feeding to understand the patterns and timing of changes in gene expression and their relationship to changes in intestinal form and function upon feeding. Our results indicate that >2,000 genes show significant changes in expression in the small intestine following feeding, including genes involved in intestinal morphology and function (e.g., hydrolases, microvillus proteins, trafficking and transport proteins), as well as genes involved in cell division and apoptosis. Extensive changes in gene expression occur surprisingly rapidly, within the first 6 h of feeding, coincide with changes in intestinal morphology, and effectively return to prefeeding levels within 10 days. Collectively, our results provide an unprecedented portrait of parallel changes in gene expression and intestinal morphology and physiology on a scale that is extreme both in the magnitude of changes, as well as in the incredibly short time frame of these changes, with up- and downregulation of expression and function occurring in the span of 10 days. Our results also identify conserved vertebrate signaling pathways that modulate these responses, which may suggest pathways for therapeutic modulation of intestinal function in humans. Copyright © 2015 the American Physiological Society.

A software solution for recording circadian oscillator features in time-lapse live cell microscopy.

PubMed

Sage, Daniel; Unser, Michael; Salmon, Patrick; Dibner, Charna

2010-07-06

Fluorescent and bioluminescent time-lapse microscopy approaches have been successfully used to investigate molecular mechanisms underlying the mammalian circadian oscillator at the single cell level. However, most of the available software and common methods based on intensity-threshold segmentation and frame-to-frame tracking are not applicable in these experiments. This is due to cell movement and dramatic changes in the fluorescent/bioluminescent reporter protein during the circadian cycle, with the lowest expression level very close to the background intensity. At present, the standard approach to analyze data sets obtained from time lapse microscopy is either manual tracking or application of generic image-processing software/dedicated tracking software. To our knowledge, these existing software solutions for manual and automatic tracking have strong limitations in tracking individual cells if their plane shifts. In an attempt to improve existing methodology of time-lapse tracking of a large number of moving cells, we have developed a semi-automatic software package. It extracts the trajectory of the cells by tracking theirs displacements, makes the delineation of cell nucleus or whole cell, and finally yields measurements of various features, like reporter protein expression level or cell displacement. As an example, we present here single cell circadian pattern and motility analysis of NIH3T3 mouse fibroblasts expressing a fluorescent circadian reporter protein. Using Circadian Gene Express plugin, we performed fast and nonbiased analysis of large fluorescent time lapse microscopy datasets. Our software solution, Circadian Gene Express (CGE), is easy to use and allows precise and semi-automatic tracking of moving cells over longer period of time. In spite of significant circadian variations in protein expression with extremely low expression levels at the valley phase, CGE allows accurate and efficient recording of large number of cell parameters, including level of reporter protein expression, velocity, direction of movement, and others. CGE proves to be useful for the analysis of widefield fluorescent microscopy datasets, as well as for bioluminescence imaging. Moreover, it might be easily adaptable for confocal image analysis by manually choosing one of the focal planes of each z-stack of the various time points of a time series. CGE is a Java plugin for ImageJ; it is freely available at: http://bigwww.epfl.ch/sage/soft/circadian/.

Polydimethylsiloxane (PDMS) modulates CD38 expression, absorbs retinoic acid and may perturb retinoid signalling.

PubMed

Futrega, Kathryn; Yu, Jianshi; Jones, Jace W; Kane, Maureen A; Lott, William B; Atkinson, Kerry; Doran, Michael R

2016-04-21

Polydimethylsiloxane (PDMS) is the most commonly used material in the manufacture of customized cell culture devices. While there is concern that uncured PDMS oligomers may leach into culture medium and/or hydrophobic molecules may be absorbed into PDMS structures, there is no consensus on how or if PDMS influences cell behaviour. We observed that human umbilical cord blood (CB)-derived CD34(+) cells expanded in standard culture medium on PDMS exhibit reduced CD38 surface expression, relative to cells cultured on tissue culture polystyrene (TCP). All-trans retinoic acid (ATRA) induces CD38 expression, and we reasoned that this hydrophobic molecule might be absorbed by PDMS. Through a series of experiments we demonstrated that ATRA-mediated CD38 expression was attenuated when cultures were maintained on PDMS. Medium pre-incubated on PDMS for extended durations resulted in a time-dependant reduction of ATRA in the medium and increasingly attenuated CD38 expression. This indicated a time-dependent absorption of ATRA into the PDMS. To better understand how PDMS might generally influence cell behaviour, Ingenuity Pathway Analysis (IPA) was used to identify potential upstream regulators. This analysis was performed for differentially expressed genes in primary cells including CD34(+) haematopoietic progenitor cells, mesenchymal stromal cells (MSC), and keratinocytes, and cell lines including prostate cancer epithelial cells (LNCaP), breast cancer epithelial cells (MCF-7), and myeloid leukaemia cells (KG1a). IPA predicted that the most likely common upstream regulator of perturbed pathways was ATRA. We demonstrate here that ATRA is absorbed by PDMS in a time-dependent manner and results in the concomitant reduced expression of CD38 on the cell surface of CB-derived CD34(+) cells.

A new space-time information expression and analysis approach based on 3S technology: a case study of China's coastland

NASA Astrophysics Data System (ADS)

Cao, Bao; Luo, Hong; Gao, Zhenji

2009-10-01

Space-time Information Expression and Analysis (SIEA) uses vivid graphic images of thinking to deal with information units according to series distribution rules with a variety of arranging, which combined with the use of information technology, powerful data-processing capabilities to carry out analysis and integration of information units. In this paper, a new SIEA approach was proposed and its model was constructed. And basic units, methodologies and steps of SIEA were discussed. Taking China's coastland as an example, the new SIEA approach were applied for the parameters of air humidity, rainfall and surface temperature from the year 1981 to 2000. The case study shows that the parameters change within month alternation, but little change within year alternation. From the view of spatial distribution, it was significantly different for the parameters in north and south of China's coastland. The new SIEA approach proposed in this paper not only has the intuitive, image characteristics, but also can solved the problem that it is difficult to express the biophysical parameters of space-time distribution using traditional charts and tables. It can reveal the complexity of the phenomenon behind the movement of things and laws of nature. And it can quantitatively analyze the phenomenon and nature law of the parameters, which inherited the advantages of graphics of traditional ways of thinking. SIEA provides a new space-time analysis and expression approach, using comprehensive 3S technologies, for the research of Earth System Science.

Host-Parasite Interactions and Purifying Selection in a Microsporidian Parasite of Honey Bees

PubMed Central

Huang, Qiang; Chen, Yan Ping; Wang, Rui Wu; Cheng, Shang; Evans, Jay D.

2016-01-01

To clarify the mechanisms of Nosema ceranae parasitism, we deep-sequenced both honey bee host and parasite mRNAs throughout a complete 6-day infection cycle. By time-series analysis, 1122 parasite genes were significantly differently expressed during the reproduction cycle, clustering into 4 expression patterns. We found reactive mitochondrial oxygen species modulator 1 of the host to be significantly down regulated during the entire infection period. Our data support the hypothesis that apoptosis of honey bee cells was suppressed during infection. We further analyzed genome-wide genetic diversity of this parasite by comparing samples collected from the same site in 2007 and 2013. The number of SNP positions per gene and the proportion of non-synonymous substitutions per gene were significantly reduced over this time period, suggesting purifying selection on the parasite genome and supporting the hypothesis that a subset of N. ceranae strains might be dominating infection. PMID:26840596

Host-Parasite Interactions and Purifying Selection in a Microsporidian Parasite of Honey Bees.

PubMed

Huang, Qiang; Chen, Yan Ping; Wang, Rui Wu; Cheng, Shang; Evans, Jay D

2016-01-01

To clarify the mechanisms of Nosema ceranae parasitism, we deep-sequenced both honey bee host and parasite mRNAs throughout a complete 6-day infection cycle. By time-series analysis, 1122 parasite genes were significantly differently expressed during the reproduction cycle, clustering into 4 expression patterns. We found reactive mitochondrial oxygen species modulator 1 of the host to be significantly down regulated during the entire infection period. Our data support the hypothesis that apoptosis of honey bee cells was suppressed during infection. We further analyzed genome-wide genetic diversity of this parasite by comparing samples collected from the same site in 2007 and 2013. The number of SNP positions per gene and the proportion of non-synonymous substitutions per gene were significantly reduced over this time period, suggesting purifying selection on the parasite genome and supporting the hypothesis that a subset of N. ceranae strains might be dominating infection.

Time Series Proteome Profiling

PubMed Central

Formolo, Catherine A.; Mintz, Michelle; Takanohashi, Asako; Brown, Kristy J.; Vanderver, Adeline; Halligan, Brian; Hathout, Yetrib

2014-01-01

This chapter provides a detailed description of a method used to study temporal changes in the endoplasmic reticulum (ER) proteome of fibroblast cells exposed to ER stress agents (tunicamycin and thapsigargin). Differential stable isotope labeling by amino acids in cell culture (SILAC) is used in combination with crude ER fractionation, SDS–PAGE and LC-MS/MS to define altered protein expression in tunicamycin or thapsigargin treated cells versus untreated cells. Treated and untreated cells are harvested at different time points, mixed at a 1:1 ratio and processed for ER fractionation. Samples containing labeled and unlabeled proteins are separated by SDS–PAGE, bands are digested with trypsin and the resulting peptides analyzed by LC-MS/MS. Proteins are identified using Bioworks software and the Swiss-Prot data-base, whereas ratios of protein expression between treated and untreated cells are quantified using ZoomQuant software. Data visualization is facilitated by GeneSpring software. proteomics PMID:21082445

A general formula for Rayleigh-Schroedinger perturbation energy utilizing a power series expansion of the quantum mechanical Hamiltonian

DOE Office of Scientific and Technical Information (OSTI.GOV)

Herbert, J.M.

1997-02-01

Perturbation theory has long been utilized by quantum chemists as a method for approximating solutions to the Schroedinger equation. Perturbation treatments represent a system`s energy as a power series in which each additional term further corrects the total energy; it is therefore convenient to have an explicit formula for the nth-order energy correction term. If all perturbations are collected into a single Hamiltonian operator, such a closed-form expression for the nth-order energy correction is well known; however, use of a single perturbed Hamiltonian often leads to divergent energy series, while superior convergence behavior is obtained by expanding the perturbed Hamiltonianmore » in a power series. This report presents a closed-form expression for the nth-order energy correction obtained using Rayleigh-Schroedinger perturbation theory and a power series expansion of the Hamiltonian.« less

MetaRNA-Seq: An Interactive Tool to Browse and Annotate Metadata from RNA-Seq Studies.

PubMed

Kumar, Pankaj; Halama, Anna; Hayat, Shahina; Billing, Anja M; Gupta, Manish; Yousri, Noha A; Smith, Gregory M; Suhre, Karsten

2015-01-01

The number of RNA-Seq studies has grown in recent years. The design of RNA-Seq studies varies from very simple (e.g., two-condition case-control) to very complicated (e.g., time series involving multiple samples at each time point with separate drug treatments). Most of these publically available RNA-Seq studies are deposited in NCBI databases, but their metadata are scattered throughout four different databases: Sequence Read Archive (SRA), Biosample, Bioprojects, and Gene Expression Omnibus (GEO). Although the NCBI web interface is able to provide all of the metadata information, it often requires significant effort to retrieve study- or project-level information by traversing through multiple hyperlinks and going to another page. Moreover, project- and study-level metadata lack manual or automatic curation by categories, such as disease type, time series, case-control, or replicate type, which are vital to comprehending any RNA-Seq study. Here we describe "MetaRNA-Seq," a new tool for interactively browsing, searching, and annotating RNA-Seq metadata with the capability of semiautomatic curation at the study level.

A Systematic Approach to Time-series Metabolite Profiling and RNA-seq Analysis of Chinese Hamster Ovary Cell Culture.

PubMed

Hsu, Han-Hsiu; Araki, Michihiro; Mochizuki, Masao; Hori, Yoshimi; Murata, Masahiro; Kahar, Prihardi; Yoshida, Takanobu; Hasunuma, Tomohisa; Kondo, Akihiko

2017-03-02

Chinese hamster ovary (CHO) cells are the primary host used for biopharmaceutical protein production. The engineering of CHO cells to produce higher amounts of biopharmaceuticals has been highly dependent on empirical approaches, but recent high-throughput "omics" methods are changing the situation in a rational manner. Omics data analyses using gene expression or metabolite profiling make it possible to identify key genes and metabolites in antibody production. Systematic omics approaches using different types of time-series data are expected to further enhance understanding of cellular behaviours and molecular networks for rational design of CHO cells. This study developed a systematic method for obtaining and analysing time-dependent intracellular and extracellular metabolite profiles, RNA-seq data (enzymatic mRNA levels) and cell counts from CHO cell cultures to capture an overall view of the CHO central metabolic pathway (CMP). We then calculated correlation coefficients among all the profiles and visualised the whole CMP by heatmap analysis and metabolic pathway mapping, to classify genes and metabolites together. This approach provides an efficient platform to identify key genes and metabolites in CHO cell culture.

Why didn't Box-Jenkins win (again)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pack, D.J.; Downing, D.J.

This paper focuses on the forecasting performance of the Box-Jenkins methodology applied to the 111 time series of the Makridakis competition. It considers the influence of the following factors: (1) time series length, (2) time-series information (autocorrelation) content, (3) time-series outliers or structural changes, (4) averaging results over time series, and (5) forecast time origin choice. It is found that the 111 time series contain substantial numbers of very short series, series with obvious structural change, and series whose histories are relatively uninformative. If these series are typical of those that one must face in practice, the real message ofmore » the competition is that univariate time series extrapolations will frequently fail regardless of the methodology employed to produce them.« less

Multifractal analysis of visibility graph-based Ito-related connectivity time series.

PubMed

Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

2016-02-01

In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

Genome‐wide gene expression dynamics of the fungal pathogen Dothistroma septosporum throughout its infection cycle of the gymnosperm host Pinus radiata

PubMed Central

Guo, Yanan; Sim, Andre D.; Kabir, M. Shahjahan; Chettri, Pranav; Ozturk, Ibrahim K.; Hunziker, Lukas; Ganley, Rebecca J.; Cox, Murray P.

2015-01-01

Summary We present genome‐wide gene expression patterns as a time series through the infection cycle of the fungal pine needle blight pathogen, Dothistroma septosporum, as it invades its gymnosperm host, Pinus radiata. We determined the molecular changes at three stages of the disease cycle: epiphytic/biotrophic (early), initial necrosis (mid) and mature sporulating lesion (late). Over 1.7 billion combined plant and fungal reads were sequenced to obtain 3.2 million fungal‐specific reads, which comprised as little as 0.1% of the sample reads early in infection. This enriched dataset shows that the initial biotrophic stage is characterized by the up‐regulation of genes encoding fungal cell wall‐modifying enzymes and signalling proteins. Later necrotrophic stages show the up‐regulation of genes for secondary metabolism, putative effectors, oxidoreductases, transporters and starch degradation. This in‐depth through‐time transcriptomic study provides our first snapshot of the gene expression dynamics that characterize infection by this fungal pathogen in its gymnosperm host. PMID:25919703

Reconstructing the temporal ordering of biological samples using microarray data.

PubMed

Magwene, Paul M; Lizardi, Paul; Kim, Junhyong

2003-05-01

Accurate time series for biological processes are difficult to estimate due to problems of synchronization, temporal sampling and rate heterogeneity. Methods are needed that can utilize multi-dimensional data, such as those resulting from DNA microarray experiments, in order to reconstruct time series from unordered or poorly ordered sets of observations. We present a set of algorithms for estimating temporal orderings from unordered sets of sample elements. The techniques we describe are based on modifications of a minimum-spanning tree calculated from a weighted, undirected graph. We demonstrate the efficacy of our approach by applying these techniques to an artificial data set as well as several gene expression data sets derived from DNA microarray experiments. In addition to estimating orderings, the techniques we describe also provide useful heuristics for assessing relevant properties of sample datasets such as noise and sampling intensity, and we show how a data structure called a PQ-tree can be used to represent uncertainty in a reconstructed ordering. Academic implementations of the ordering algorithms are available as source code (in the programming language Python) on our web site, along with documentation on their use. The artificial 'jelly roll' data set upon which the algorithm was tested is also available from this web site. The publicly available gene expression data may be found at http://genome-www.stanford.edu/cellcycle/ and http://caulobacter.stanford.edu/CellCycle/.

MicroRNA signatures in B-cell lymphomas

PubMed Central

Di Lisio, L; Sánchez-Beato, M; Gómez-López, G; Rodríguez, M E; Montes-Moreno, S; Mollejo, M; Menárguez, J; Martínez, M A; Alves, F J; Pisano, D G; Piris, M A; Martínez, N

2012-01-01

Accurate lymphoma diagnosis, prognosis and therapy still require additional markers. We explore the potential relevance of microRNA (miRNA) expression in a large series that included all major B-cell non-Hodgkin lymphoma (NHL) types. The data generated were also used to identify miRNAs differentially expressed in Burkitt lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL) samples. A series of 147 NHL samples and 15 controls were hybridized on a human miRNA one-color platform containing probes for 470 human miRNAs. Each lymphoma type was compared against the entire set of NHLs. BL was also directly compared with DLBCL, and 43 preselected miRNAs were analyzed in a new series of routinely processed samples of 28 BLs and 43 DLBCLs using quantitative reverse transcription-polymerase chain reaction. A signature of 128 miRNAs enabled the characterization of lymphoma neoplasms, reflecting the lymphoma type, cell of origin and/or discrete oncogene alterations. Comparative analysis of BL and DLBCL yielded 19 differentially expressed miRNAs, which were confirmed in a second confirmation series of 71 paraffin-embedded samples. The set of differentially expressed miRNAs found here expands the range of potential diagnostic markers for lymphoma diagnosis, especially when differential diagnosis of BL and DLBCL is required. PMID:22829247

Diagnostic value of progesterone receptor, p16, p53 and pHH3 expression in uterine atypical leiomyoma.

PubMed

Liang, Yun; Zhang, Xiaofei; Chen, Xiaoduan; Lü, Weiguo

2015-01-01

The differential diagnosis between atypical leiomyoma and leiomyosarcoma may be hard based on morphological criterion at times. It would be helpful to find out biomarkers that can be used to distinguish them. The aim of the study was to investigate the diagnostic value of progesterone receptor (PR), p16, p53 and pHH3 expression in a series of uterine smooth muscle tumors. Immunohistochemical expression of PR, p16, p53 and pHH3 was investigated on 32 atypical leiomyomas, 15 leiomyosarcomas and 15 usual leomyomas. The difference in expression was compared between atypical leiomyoma and other groups. The expression of PR, p16, and pHH3 was found significantly different between atypical leiomyomas and leiomyosarcomas, but lack of significant difference between atypical leiomyomas and usual leiomyomas. There was no significant difference with regard to p53 distribution among these uterine smooth muscle tumors. High p16, pHH3 expression and low PR expression preferred the diagnosis of leiomyosarcoma. The panel of antibodies used in this study is a useful complementary analysis in the assessment of problematic uterine smooth muscle tumors.

Diagnostic value of progesterone receptor, p16, p53 and pHH3 expression in uterine atypical leiomyoma

PubMed Central

Liang, Yun; Zhang, Xiaofei; Chen, Xiaoduan; Lü, Weiguo

2015-01-01

The differential diagnosis between atypical leiomyoma and leiomyosarcoma may be hard based on morphological criterion at times. It would be helpful to find out biomarkers that can be used to distinguish them. The aim of the study was to investigate the diagnostic value of progesterone receptor (PR), p16, p53 and pHH3 expression in a series of uterine smooth muscle tumors. Immunohistochemical expression of PR, p16, p53 and pHH3 was investigated on 32 atypical leiomyomas, 15 leiomyosarcomas and 15 usual leomyomas. The difference in expression was compared between atypical leiomyoma and other groups. The expression of PR, p16, and pHH3 was found significantly different between atypical leiomyomas and leiomyosarcomas, but lack of significant difference between atypical leiomyomas and usual leiomyomas. There was no significant difference with regard to p53 distribution among these uterine smooth muscle tumors. High p16, pHH3 expression and low PR expression preferred the diagnosis of leiomyosarcoma. The panel of antibodies used in this study is a useful complementary analysis in the assessment of problematic uterine smooth muscle tumors. PMID:26261614

An asymptotic theory for cross-correlation between auto-correlated sequences and its application on neuroimaging data.

PubMed

Zhou, Yunyi; Tao, Chenyang; Lu, Wenlian; Feng, Jianfeng

2018-04-20

Functional connectivity is among the most important tools to study brain. The correlation coefficient, between time series of different brain areas, is the most popular method to quantify functional connectivity. Correlation coefficient in practical use assumes the data to be temporally independent. However, the time series data of brain can manifest significant temporal auto-correlation. A widely applicable method is proposed for correcting temporal auto-correlation. We considered two types of time series models: (1) auto-regressive-moving-average model, (2) nonlinear dynamical system model with noisy fluctuations, and derived their respective asymptotic distributions of correlation coefficient. These two types of models are most commonly used in neuroscience studies. We show the respective asymptotic distributions share a unified expression. We have verified the validity of our method, and shown our method exhibited sufficient statistical power for detecting true correlation on numerical experiments. Employing our method on real dataset yields more robust functional network and higher classification accuracy than conventional methods. Our method robustly controls the type I error while maintaining sufficient statistical power for detecting true correlation in numerical experiments, where existing methods measuring association (linear and nonlinear) fail. In this work, we proposed a widely applicable approach for correcting the effect of temporal auto-correlation on functional connectivity. Empirical results favor the use of our method in functional network analysis. Copyright © 2018. Published by Elsevier B.V.

Regenerating time series from ordinal networks.

PubMed

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Regenerating time series from ordinal networks

NASA Astrophysics Data System (ADS)

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

PSA-NCAM expression in the teleost optic tectum is related to ecological niche and use of vision in finding food.

PubMed

Labak, I; Pavić, V; Zjalić, M; Blažetić, S; Viljetić, B; Merdić, E; Heffer, M

2017-08-01

In this study, tangential migration and neuronal connectivity organization were analysed in the optic tectum of seven different teleosts through the expression of polysialylated neural cell adhesion molecule (PSA-NCAM) in response to ecological niche and use of vision. Reduced PSA-NCAM expression in rainbow trout Oncorhynchus mykiss optic tectum occurred in efferent layers, while in pike Esox lucius and zebrafish Danio rerio it occurred in afferent and efferent layers. Zander Sander lucioperca and European eel Anguilla anguilla had very low PSA-NCAM expression in all tectal layers except in the stratum marginale. Common carp Cyprinus carpio and wels catfish Silurus glanis had the same intensity of PSA-NCAM expression in all tectal layers. The optic tectum of all studied fishes was also a site of tangential migration with sustained PSA-NCAM and c-series ganglioside expression. Anti-c-series ganglioside immunoreactivity was observed in all tectal layers of all analysed fishes, even in layers where PSA-NCAM expression was reduced. Since the optic tectum is indispensable for visually guided prey capture, stabilization of synaptic contact and decrease of neurogenesis and tangential migration in the visual map are an expected adjustment to ecological niche. The authors hypothesize that this stabilization would probably be achieved by down-regulation of PSA-NCAM rather than c-series of ganglioside. © 2017 The Fisheries Society of the British Isles.

GPS Position Time Series @ JPL

NASA Technical Reports Server (NTRS)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

Gene expression profiling in the early phases of DMD: a constant molecular signature characterizes DMD muscle from early postnatal life throughout disease progression.

PubMed

Pescatori, Mario; Broccolini, Aldobrando; Minetti, Carlo; Bertini, Enrico; Bruno, Claudio; D'amico, Adele; Bernardini, Camilla; Mirabella, Massimiliano; Silvestri, Gabriella; Giglio, Vincenzo; Modoni, Anna; Pedemonte, Marina; Tasca, Giorgio; Galluzzi, Giuliana; Mercuri, Eugenio; Tonali, Pietro A; Ricci, Enzo

2007-04-01

Genome-wide gene expression profiling of skeletal muscle from Duchenne muscular dystrophy (DMD) patients has been used to describe muscle tissue alterations in DMD children older than 5 years. By studying the expression profile of 19 patients younger than 2 years, we describe with high resolution the gene expression signature that characterizes DMD muscle during the initial or "presymptomatic" phase of the disease. We show that in the first 2 years of the disease, DMD muscle is already set to express a distinctive gene expression pattern considerably different from the one expressed by normal, age-matched muscle. This "dystrophic" molecular signature is characterized by a coordinate induction of genes involved in the inflammatory response, extracellular matrix (ECM) remodeling and muscle regeneration, and the reduced transcription of those involved in energy metabolism. Despite the lower degree of muscle dysfunction experienced, our younger patients showed abnormal expression of most of the genes reported as differentially expressed in more advanced stages of the disease. By analyzing our patients as a time series, we provide evidence that some genes, including members of three pathways involved in morphogenetic signaling-Wnt, Notch, and BMP-are progressively induced or repressed in the natural history of DMD.

Dynamic transcriptomic analysis in hircine longissimus dorsi muscle from fetal to neonatal development stages.

PubMed

Zhan, Siyuan; Zhao, Wei; Song, Tianzeng; Dong, Yao; Guo, Jiazhong; Cao, Jiaxue; Zhong, Tao; Wang, Linjie; Li, Li; Zhang, Hongping

2018-01-01

Muscle growth and development from fetal to neonatal stages consist of a series of delicately regulated and orchestrated changes in expression of genes. In this study, we performed whole transcriptome profiling based on RNA-Seq of caprine longissimus dorsi muscle tissue obtained from prenatal stages (days 45, 60, and 105 of gestation) and neonatal stage (the 3-day-old newborn) to identify genes that are differentially expressed and investigate their temporal expression profiles. A total of 3276 differentially expressed genes (DEGs) were identified (Q value < 0.01). Time-series expression profile clustering analysis indicated that DEGs were significantly clustered into eight clusters which can be divided into two classes (Q value < 0.05), class I profiles with downregulated patterns and class II profiles with upregulated patterns. Based on cluster analysis, GO enrichment analysis found that 75, 25, and 8 terms to be significantly enriched in biological process (BP), cellular component (CC), and molecular function (MF) categories in class I profiles, while 35, 21, and 8 terms to be significantly enriched in BP, CC, and MF in class II profiles. KEGG pathway analysis revealed that DEGs from class I profiles were significantly enriched in 22 pathways and the most enriched pathway was Rap1 signaling pathway. DEGs from class II profiles were significantly enriched in 17 pathways and the mainly enriched pathway was AMPK signaling pathway. Finally, six selected DEGs from our sequencing results were confirmed by qPCR. Our study provides a comprehensive understanding of the molecular mechanisms during goat skeletal muscle development from fetal to neonatal stages and valuable information for future studies of muscle development in goats.

Cognitive "babyness": developmental differences in the power of young children's supernatural thinking to influence positive and negative affect.

PubMed

Periss, Virginia; Blasi, Carlos Hernández; Bjorklund, David F

2012-09-01

Perceptions of maturational status may play an important role in facilitating caretaking and resources toward children expressing them. Previous work has revealed evidence that cues of cognitive immaturity foster positive perceptions in adults toward young children at a time during their lives when they are most dependent on adult care. In the current series of studies, the authors investigated when during development these biases emerge. They tested American and Spanish adolescents ranging from 10 to 17 years of age. Each participant rated a series of vignettes presenting different expressions of immature and mature thinking attributed to young children. Results revealed that older adolescents performed similarly to adults tested in previous studies (D. F. Bjorklund, C. Hernández Blasi, & V. A. Periss, 2010), rating positively expressions of supernatural thinking (e.g., animism) compared with other forms of immature cognition labeled as natural (e.g., overestimation). Both male and female participants 14 years and older favored children expressing the immature supernatural cognition on traits reflecting positive affect (e.g., endearing, likeable), while associating greater negative affect (e.g., sneaky, impatient with) with children expressing immature natural cognition. However, younger adolescents consistently rated all forms of immature thinking less positively than mature thinking, suggesting that a positive bias for some forms of immature thinking develops during adolescence. Based on an evolutionary developmental framework, the authors suggest that supernatural thinking may have a unique role in humans, fostering positive perceptions of young children in older adolescents (and adults) as they prepare themselves for the possible role of parenthood. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Butyrate modulating effects on pro-inflammatory pathways in human intestinal epithelial cells.

PubMed

Elce, A; Amato, F; Zarrilli, F; Calignano, A; Troncone, R; Castaldo, G; Canani, R B

2017-10-13

Butyrate acts as energy source for intestinal epithelial cells and as key mediator of several immune processes, modulating gene expression mainly through histone deacetylation inhibition. Thanks to these effects, butyrate has been proposed for the treatment of many intestinal diseases. Aim of this study was to investigate the effect of butyrate on the expression of a large series of target genes encoding proteins involved in pro-inflammatory pathways. We performed quantitative real-time-PCR analysis of the expression of 86 genes encoding proteins bearing to pro-inflammatory pathways, before and after butyrate exposure, in primary epithelial cells derived from human small intestine and colon. Butyrate significantly down-regulated the expression of genes involved in inflammatory response, among which nuclear factor kappa beta, interferon-gamma, Toll like 2 receptor and tumour necrosis factor-alpha. Further confirmations of these data, including studies at protein level, would support the use of butyrate as effective therapeutic strategy in intestinal inflammatory disorders.

Online Analytical Processing (OLAP): A Fast and Effective Data Mining Tool for Gene Expression Databases

PubMed Central

2005-01-01

Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, a devastating pest of soybean. The data for these experiments is stored in the soybean genomics and microarray database (SGMD). A number of candidate resistance genes and pathways were found. Compared to traditional cluster analysis of gene expression data, OLAP was more effective and faster in finding biologically meaningful information. OLAP is available from a number of vendors and can work with any relational database management system through OLE DB. PMID:16046824

Inter-laboratory quality control for hormone-dependent gene expression in human breast tumors using real-time reverse transcription-polymerase chain reaction.

PubMed

de Cremoux, P; Bieche, I; Tran-Perennou, C; Vignaud, S; Boudou, E; Asselain, B; Lidereau, R; Magdelénat, H; Becette, V; Sigal-Zafrani, B; Spyratos, F

2004-09-01

Quantitative reverse transcription-polymerase chain reaction (RT-PCR) used to detect minor changes in specific mRNA concentrations may be associated with poor reproducibility. Stringent quality control is therefore essential at each step of the protocol, including the PCR procedure. We performed inter-laboratory quality control of quantitative PCR between two independent laboratories, using in-house RT-PCR assays on a series of hormone-related target genes in a retrospective consecutive series of 79 breast tumors. Total RNA was reverse transcribed in a single center. Calibration curves were performed for five target genes (estrogen receptor (ER)alpha, ERbeta, progesterone receptor (PR), CYP19 (aromatase) and Ki 67) and for two reference genes (human acidic ribosomal phosphoprotein PO (RPLPO) and TATA box-binding protein (TBP)). Amplification efficiencies of the calibrator were determined for each run and used to calculate mRNA expression. Correlation coefficients were evaluated for each target and each reference gene. A good correlation was observed for all target and reference genes in both centers using their own protocols and kits (P < 0.0001). The correlation coefficients ranged from 0.90 to 0.98 for the various target genes in the two centers. A good correlation was observed between the level of expression of the ERalpha and the PR transcripts (P < 0.001). A weak inverse correlation was observed in both centers between ERalpha and ERbeta levels, but only when TBP was the reference gene. No other correlation was observed with other parameters. Real-time PCR assays allow convenient quantification of target mRNA transcripts and quantification of target-derived nucleic acids in clinical specimens. This study addresses the importance of inter-laboratory quality controls for the use of a panel of real-time PCR assays devoted to clinical samples and protocols and to ensure their appropriate accuracy. This can also facilitate exchanges and multicenter comparison of data.

A general framework for time series data mining based on event analysis: application to the medical domains of electroencephalography and stabilometry.

PubMed

Lara, Juan A; Lizcano, David; Pérez, Aurora; Valente, Juan P

2014-10-01

There are now domains where information is recorded over a period of time, leading to sequences of data known as time series. In many domains, like medicine, time series analysis requires to focus on certain regions of interest, known as events, rather than analyzing the whole time series. In this paper, we propose a framework for knowledge discovery in both one-dimensional and multidimensional time series containing events. We show how our approach can be used to classify medical time series by means of a process that identifies events in time series, generates time series reference models of representative events and compares two time series by analyzing the events they have in common. We have applied our framework on time series generated in the areas of electroencephalography (EEG) and stabilometry. Framework performance was evaluated in terms of classification accuracy, and the results confirmed that the proposed schema has potential for classifying EEG and stabilometric signals. The proposed framework is useful for discovering knowledge from medical time series containing events, such as stabilometric and electroencephalographic time series. These results would be equally applicable to other medical domains generating iconographic time series, such as, for example, electrocardiography (ECG). Copyright © 2014 Elsevier Inc. All rights reserved.

Critical Issues in the Understanding of Young Elementary School Students at Risk for Problems in Written Expression: Introduction to the Special Series.

PubMed

Coker, David L; Kim, Young-Suk Grace

In this introduction to the special series "Critical Issues in the Understanding of Young Elementary School Students at Risk for Problems in Written Expression," we consider some of the contextual factors that have changed since a similar special issue was published in the Journal of Learning Disabilities in 2002. We also explore how the five articles included in this special series address the following important themes: early writing development, identification of students with writing difficulties, and effective interventions for struggling writers. In conclusion, we envision future directions to advance the field.

Global gene expression of Prochlorococcus ecotypes in response to changes in nitrogen availability

PubMed Central

Tolonen, Andrew C; Aach, John; Lindell, Debbie; Johnson, Zackary I; Rector, Trent; Steen, Robert; Church, George M; Chisholm, Sallie W

2006-01-01

Nitrogen (N) often limits biological productivity in the oceanic gyres where Prochlorococcus is the most abundant photosynthetic organism. The Prochlorococcus community is composed of strains, such as MED4 and MIT9313, that have different N utilization capabilities and that belong to ecotypes with different depth distributions. An interstrain comparison of how Prochlorococcus responds to changes in ambient nitrogen is thus central to understanding its ecology. We quantified changes in MED4 and MIT9313 global mRNA expression, chlorophyll fluorescence, and photosystem II photochemical efficiency (Fv/Fm) along a time series of increasing N starvation. In addition, the global expression of both strains growing in ammonium-replete medium was compared to expression during growth on alternative N sources. There were interstrain similarities in N regulation such as the activation of a putative NtcA regulon during N stress. There were also important differences between the strains such as in the expression patterns of carbon metabolism genes, suggesting that the two strains integrate N and C metabolism in fundamentally different ways. PMID:17016519

Infrasound exposure induces apoptosis of rat cardiac myocytes by regulating the expression of apoptosis-related proteins.

PubMed

Pei, Zhao-Hui; Chen, Bao-Ying; Tie, Ru; Zhang, Hai-Feng; Zhao, Ge; Qu, Ping; Zhu, Xiao-Xing; Zhu, Miao-Zhang; Yu, Jun

2011-12-01

It has been reported that exposure to infrasound causes cardiac dysfunction. Allowing for the key role of apoptosis in the pathogenesis of cardiovascular diseases, the objective of this study was to investigate the apoptotic effects of infrasound. Cardiac myocytes cultured from neonatal rats were exposed to infrasound of 5 Hz at 130 dB. The apoptosis was determined by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling. Also, the expression levels of a series of apoptosis-related proteins were detected. As a result, infrasound induced apoptosis of cultured rat cardiac myocytes in a time-dependant manner. The expression of proapoptotic proteins such as Bax, caspase-3, caspase-8, caspase-9, and FAS was significantly up-regulated, with concomitant down-regulated expression of antiapoptotic proteins such as Bcl-x, and the inhibitory apoptosis proteins family proteins including XIAP, cIAP-1, and cIAP-2. The expression of poly (ADP-ribose) polymerase and β-catenin, which are the substrate proteins of caspase-3, was significantly decreased. In conclusion, infrasound is an apoptotic inducer of cardiac myocytes.

Systems perspectives on erythromycin biosynthesis by comparative genomic and transcriptomic analyses of S. erythraea E3 and NRRL23338 strains

PubMed Central

2013-01-01

Background S. erythraea is a Gram-positive filamentous bacterium used for the industrial-scale production of erythromycin A which is of high clinical importance. In this work, we sequenced the whole genome of a high-producing strain (E3) obtained by random mutagenesis and screening from the wild-type strain NRRL23338, and examined time-series expression profiles of both E3 and NRRL23338. Based on the genomic data and transcriptpmic data of these two strains, we carried out comparative analysis of high-producing strain and wild-type strain at both the genomic level and the transcriptomic level. Results We observed a large number of genetic variants including 60 insertions, 46 deletions and 584 single nucleotide variations (SNV) in E3 in comparison with NRRL23338, and the analysis of time series transcriptomic data indicated that the genes involved in erythromycin biosynthesis and feeder pathways were significantly up-regulated during the 60 hours time-course. According to our data, BldD, a previously identified ery cluster regulator, did not show any positive correlations with the expression of ery cluster, suggesting the existence of alternative regulation mechanisms of erythromycin synthesis in S. erythraea. Several potential regulators were then proposed by integration analysis of genomic and transcriptomic data. Conclusion This is a demonstration of the functional comparative genomics between an industrial S. erythraea strain and the wild-type strain. These findings help to understand the global regulation mechanisms of erythromycin biosynthesis in S. erythraea, providing useful clues for genetic and metabolic engineering in the future. PMID:23902230

Unmixing of fluorescence spectra to resolve quantitative time-series measurements of gene expression in plate readers.

PubMed

Lichten, Catherine A; White, Rachel; Clark, Ivan B N; Swain, Peter S

2014-02-03

To connect gene expression with cellular physiology, we need to follow levels of proteins over time. Experiments typically use variants of Green Fluorescent Protein (GFP), and time-series measurements require specialist expertise if single cells are to be followed. Fluorescence plate readers, however, a standard in many laboratories, can in principle provide similar data, albeit at a mean, population level. Nevertheless, extracting the average fluorescence per cell is challenging because autofluorescence can be substantial. Here we propose a general method for correcting plate reader measurements of fluorescent proteins that uses spectral unmixing and determines both the fluorescence per cell and the errors on that fluorescence. Combined with strain collections, such as the GFP fusion collection for budding yeast, our methodology allows quantitative measurements of protein levels of up to hundreds of genes and therefore provides complementary data to high throughput studies of transcription. We illustrate the method by following the induction of the GAL genes in Saccharomyces cerevisiae for over 20 hours in different sugars and argue that the order of appearance of the Leloir enzymes may be to reduce build-up of the toxic intermediate galactose-1-phosphate. Further, we quantify protein levels of over 40 genes, again over 20 hours, after cells experience a change in carbon source (from glycerol to glucose). Our methodology is sensitive, scalable, and should be applicable to other organisms. By allowing quantitative measurements on a per cell basis over tens of hours and over hundreds of genes, it should increase our understanding of the dynamic changes that drive cellular behaviour.

Unmixing of fluorescence spectra to resolve quantitative time-series measurements of gene expression in plate readers

PubMed Central

2014-01-01

Background To connect gene expression with cellular physiology, we need to follow levels of proteins over time. Experiments typically use variants of Green Fluorescent Protein (GFP), and time-series measurements require specialist expertise if single cells are to be followed. Fluorescence plate readers, however, a standard in many laboratories, can in principle provide similar data, albeit at a mean, population level. Nevertheless, extracting the average fluorescence per cell is challenging because autofluorescence can be substantial. Results Here we propose a general method for correcting plate reader measurements of fluorescent proteins that uses spectral unmixing and determines both the fluorescence per cell and the errors on that fluorescence. Combined with strain collections, such as the GFP fusion collection for budding yeast, our methodology allows quantitative measurements of protein levels of up to hundreds of genes and therefore provides complementary data to high throughput studies of transcription. We illustrate the method by following the induction of the GAL genes in Saccharomyces cerevisiae for over 20 hours in different sugars and argue that the order of appearance of the Leloir enzymes may be to reduce build-up of the toxic intermediate galactose-1-phosphate. Further, we quantify protein levels of over 40 genes, again over 20 hours, after cells experience a change in carbon source (from glycerol to glucose). Conclusions Our methodology is sensitive, scalable, and should be applicable to other organisms. By allowing quantitative measurements on a per cell basis over tens of hours and over hundreds of genes, it should increase our understanding of the dynamic changes that drive cellular behaviour. PMID:24495318

Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks

NASA Astrophysics Data System (ADS)

Jia, Chen; Qian, Hong; Chen, Min; Zhang, Michael Q.

2018-03-01

The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression.

Detection of a sudden change of the field time series based on the Lorenz system.

PubMed

Da, ChaoJiu; Li, Fang; Shen, BingLu; Yan, PengCheng; Song, Jian; Ma, DeShan

2017-01-01

We conducted an exploratory study of the detection of a sudden change of the field time series based on the numerical solution of the Lorenz system. First, the time when the Lorenz path jumped between the regions on the left and right of the equilibrium point of the Lorenz system was quantitatively marked and the sudden change time of the Lorenz system was obtained. Second, the numerical solution of the Lorenz system was regarded as a vector; thus, this solution could be considered as a vector time series. We transformed the vector time series into a time series using the vector inner product, considering the geometric and topological features of the Lorenz system path. Third, the sudden change of the resulting time series was detected using the sliding t-test method. Comparing the test results with the quantitatively marked time indicated that the method could detect every sudden change of the Lorenz path, thus the method is effective. Finally, we used the method to detect the sudden change of the pressure field time series and temperature field time series, and obtained good results for both series, which indicates that the method can apply to high-dimension vector time series. Mathematically, there is no essential difference between the field time series and vector time series; thus, we provide a new method for the detection of the sudden change of the field time series.

Frequency domain system identification of helicopter rotor dynamics incorporating models with time periodic coefficients

NASA Astrophysics Data System (ADS)

Hwang, Sunghwan

1997-08-01

One of the most prominent features of helicopter rotor dynamics in forward flight is the periodic coefficients in the equations of motion introduced by the rotor rotation. The frequency response characteristics of such a linear time periodic system exhibits sideband behavior, which is not the case for linear time invariant systems. Therefore, a frequency domain identification methodology for linear systems with time periodic coefficients was developed, because the linear time invariant theory cannot account for sideband behavior. The modulated complex Fourier series was introduced to eliminate the smearing effect of Fourier series expansions of exponentially modulated periodic signals. A system identification theory was then developed using modulated complex Fourier series expansion. Correlation and spectral density functions were derived using the modulated complex Fourier series expansion for linear time periodic systems. Expressions of the identified harmonic transfer function were then formulated using the spectral density functions both with and without additive noise processes at input and/or output. A procedure was developed to identify parameters of a model to match the frequency response characteristics between measured and estimated harmonic transfer functions by minimizing an objective function defined in terms of the trace of the squared frequency response error matrix. Feasibility was demonstrated by the identification of the harmonic transfer function and parameters for helicopter rigid blade flapping dynamics in forward flight. This technique is envisioned to satisfy the needs of system identification in the rotating frame, especially in the context of individual blade control. The technique was applied to the coupled flap-lag-inflow dynamics of a rigid blade excited by an active pitch link. The linear time periodic technique results were compared with the linear time invariant technique results. Also, the effect of noise processes and initial parameter guess on the identification procedure were investigated. To study the effect of elastic modes, a rigid blade with a trailing edge flap excited by a smart actuator was selected and system parameters were successfully identified, but with some expense of computational storage and time. Conclusively, the linear time periodic technique substantially improved the identified parameter accuracy compared to the linear time invariant technique. Also, the linear time periodic technique was robust to noises and initial guess of parameters. However, an elastic mode of higher frequency relative to the system pumping frequency tends to increase the computer storage requirement and computing time.

Evaluation of CryoSat-2 Measurements for the Monitoring of Large River Water Levels

NASA Astrophysics Data System (ADS)

Bercher, Nicolas; Calmant, Stephane; Picot, Nicolas; Seyler, Frederique; Fleury, Sara

2013-09-01

In this study, and maybe for the first time, we explore the ability of CryoSat-2 satellite to monitor the water level of large rivers. We focus on a section of 500 km of the Madeira river (Amazon basin), around the town of Manicore, cf. Fig.1.Due to the drifting orbit of the mission, the usual concept of "virtual station" vanishes and data are to be extracted within polygons that delineate the riverbeds. This results in spatio-temporal time series of the river water level, expressed as a function of both space (distance to the ocean) and time.We use Cryosat-2 low resolution mode (LRM) data processed with an Ice2 retracker, i.e., the content of the upcoming IOP/GOP ocean product from ESA [1]. For this study, we use demonstration samples (year 2011 on our validation area), processed by the so-called Cryosat Processing Prototype developed by CNES in the framework of the Sentinel-3 Project from ESA [5] [4]. At the time of this study, the product came with no corrections ("solid earth tide", atmosphere, etc.), .Validation is performed on (1) river water level pseudo time series and (2) river pseudo profile. An overview of the spatio-temporal time series is also given in 2D and 3D plots. Despite the lack of geophysical corrections, results are really promising (Std 0.51 m) and are challenging those obtained by Envisat (Std 0.43 m) and Jason-2 (Std 0.47 m) on nearby virtual stations.We also demonstrate the potential of the CryoSat-2 and the appropriateness of its drifting orbit to map rivers topography and derive water levels "at anytime and anywhere" , a major topic of interest regarding hydrological propagation models and the preparation of the SWOT mission.

DigOut: viewing differential expression genes as outliers.

PubMed

Yu, Hui; Tu, Kang; Xie, Lu; Li, Yuan-Yuan

2010-12-01

With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.

Duality between Time Series and Networks

PubMed Central

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

Medium-throughput processing of whole mount in situ hybridisation experiments into gene expression domains.

PubMed

Crombach, Anton; Cicin-Sain, Damjan; Wotton, Karl R; Jaeger, Johannes

2012-01-01

Understanding the function and evolution of developmental regulatory networks requires the characterisation and quantification of spatio-temporal gene expression patterns across a range of systems and species. However, most high-throughput methods to measure the dynamics of gene expression do not preserve the detailed spatial information needed in this context. For this reason, quantification methods based on image bioinformatics have become increasingly important over the past few years. Most available approaches in this field either focus on the detailed and accurate quantification of a small set of gene expression patterns, or attempt high-throughput analysis of spatial expression through binary pattern extraction and large-scale analysis of the resulting datasets. Here we present a robust, "medium-throughput" pipeline to process in situ hybridisation patterns from embryos of different species of flies. It bridges the gap between high-resolution, and high-throughput image processing methods, enabling us to quantify graded expression patterns along the antero-posterior axis of the embryo in an efficient and straightforward manner. Our method is based on a robust enzymatic (colorimetric) in situ hybridisation protocol and rapid data acquisition through wide-field microscopy. Data processing consists of image segmentation, profile extraction, and determination of expression domain boundary positions using a spline approximation. It results in sets of measured boundaries sorted by gene and developmental time point, which are analysed in terms of expression variability or spatio-temporal dynamics. Our method yields integrated time series of spatial gene expression, which can be used to reverse-engineer developmental gene regulatory networks across species. It is easily adaptable to other processes and species, enabling the in silico reconstitution of gene regulatory networks in a wide range of developmental contexts.

Dollar$ & $en$e. Part VI: Knowledge management: the state of the art.

PubMed

Wilkinson, I

2001-01-01

In Part I of this series, I introduced the concept of memes (1). Memes are ideas or concepts--the information world equivalent of genes. The goal of this series of articles is to infect you with memes so you will assimilate, translate, and express them. No matter what our area of expertise or "-ology," we all are in the information business. Our goal is to be in the wisdom business. In the previous articles in this series, I showed that when we convert raw data into wisdom, we are moving along a value chain. Each step in the chain adds a different amount of value to the final product: timely, relevant, accurate, and precise knowledge that then can be applied to create the ultimate product in the value chain--wisdom. In part II of this series, I introduced a set of memes for measuring the cost of adding value (2). In part III of this series, I presented a new set of memes for measuring the added value of knowledge, i.e., intellectual capital (3). In part IV of this series, I discussed practical knowledge management tools for measuring the value of people, structural, and customer capital (4). In part V of this series, I applied intellectual capital and knowledge management concepts at the individual level, to help answer a fundamental question: what is my added value (5)? In the final part of this series, I will review the state of intellectual capital and knowledge management development to date and outline the direction of current knowledge management initiatives and research projects.

svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification.

PubMed

Zhang, Wensheng; Edwards, Andrea; Fan, Wei; Zhu, Dongxiao; Zhang, Kun

2010-06-22

Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods. Using the singular value decomposition (SVD) technique, we developed a new computational tool, named svdPPCS (SVD-based Pattern Pairing and Chart Splitting), to identify conserved and divergent co-expression modules of two sets of microarray experiments. In the proposed methods, gene modules are identified by splitting the two-way chart coordinated with a pair of left singular vectors factorized from the gene expression matrices of the two biological categories. Importantly, the cutoffs are determined by a data-driven algorithm using the well-defined statistic, SVD-p. The implementation was illustrated on two time series microarray data sets generated from the samples of accessory gland (ACG) and malpighian tubule (MT) tissues of the line W118 of M. drosophila. Two conserved modules and six divergent modules, each of which has a unique characteristic profile across tissue kinds and aging processes, were identified. The number of genes contained in these models ranged from five to a few hundred. Three to over a hundred GO terms were over-represented in individual modules with FDR < 0.1. One divergent module suggested the tissue-specific relationship between the expressions of mitochondrion-related genes and the aging process. This finding, together with others, may be of biological significance. The validity of the proposed SVD-based method was further verified by a simulation study, as well as the comparisons with regression analysis and cubic spline regression analysis plus PAM based clustering. svdPPCS is a novel computational tool for the comparative analysis of transcriptional profiling. It especially fits the comparison of time series data of related organisms or different tissues of the same organism under equivalent or similar experimental conditions. The general scheme can be directly extended to the comparisons of multiple data sets. It also can be applied to the integration of data sets from different platforms and of different sources.

Time and Temperature Stability of Silver-Coated Ceramics for Hydrogen Maser Resonant Cavities

DTIC Science & Technology

1988-12-01

observations. EXPERIMENTAL TECHNIQUE Samples of Cervit C-101 (Owens-Illinois Corp.), Zerodur (Schott Glass Corp.) and ULE (Corning Glass Works) were...created using a He-Ne laser interferometerl41. The fringe patterns were photographed, manually digitized, and analyzed by a computer that fits a series...Material ES l~ (101°~/m2) Zerodur 9.1 0.24 Cervit C101 9.2 ULE 6.8 RESULTS SAMPLE CURVATURE Fig. 1 shows the curvature, as expressed by the

Regulation of broad by the Notch pathway affects timing of follicle cell development

PubMed Central

Jia, Dongyu; Tamori, Yoichiro; Pyrowolakis, George; Deng, Wu-Min

2014-01-01

During Drosophila oogenesis, activation of Notch signaling in the follicular epithelium (FE) around stage 6 of oogenesis is essential for entry into the endocycle and a series of other changes such as cell differentiation and migration of subsets of the follicle cells. Notch induces the expression of zinc finger protein Hindsight and suppresses homeodomain protein Cut to regulate the mitotic/endocycle (ME) switch. Here we report that broad (br), encoding a small group of zinc-finger transcription factors resulting from alternative splicing, is a transcriptional target of Notch nuclear effector Suppressor of Hairless (Su(H)). The early pattern of Br in the FE, uniformly expressed except in the polar cells, is established by Notch signaling around stage 6, through the binding of Su(H) to the br early enhancer (brE) region. Mutation of the Su(H) binding site leads to a significant reduction of brE reporter expression in follicle cells undergoing the endocycle. Chromatin immunoprecipitation results further confirm Su(H) binding to the br early enhancer. Consistent with its expression in follicle cells during midoogenesis, loss of br function results in a delayed entry into the endocycle. Our findings suggest an important role of br in the timing of follicle cell development, and its transcriptional regulation by the Notch pathway. PMID:24815210

Discovering graphical Granger causality using the truncating lasso penalty

PubMed Central

Shojaie, Ali; Michailidis, George

2010-01-01

Motivation: Components of biological systems interact with each other in order to carry out vital cell functions. Such information can be used to improve estimation and inference, and to obtain better insights into the underlying cellular mechanisms. Discovering regulatory interactions among genes is therefore an important problem in systems biology. Whole-genome expression data over time provides an opportunity to determine how the expression levels of genes are affected by changes in transcription levels of other genes, and can therefore be used to discover regulatory interactions among genes. Results: In this article, we propose a novel penalization method, called truncating lasso, for estimation of causal relationships from time-course gene expression data. The proposed penalty can correctly determine the order of the underlying time series, and improves the performance of the lasso-type estimators. Moreover, the resulting estimate provides information on the time lag between activation of transcription factors and their effects on regulated genes. We provide an efficient algorithm for estimation of model parameters, and show that the proposed method can consistently discover causal relationships in the large p, small n setting. The performance of the proposed model is evaluated favorably in simulated, as well as real, data examples. Availability: The proposed truncating lasso method is implemented in the R-package ‘grangerTlasso’ and is freely available at http://www.stat.lsa.umich.edu/∼shojaie/ Contact: shojaie@umich.edu PMID:20823316

Metabolic switches and adaptations deduced from the proteomes of Streptomyces coelicolor wild type and phoP mutant grown in batch culture.

PubMed

Thomas, Louise; Hodgson, David A; Wentzel, Alexander; Nieselt, Kay; Ellingsen, Trond E; Moore, Jonathan; Morrissey, Edward R; Legaie, Roxane; Wohlleben, Wolfgang; Rodríguez-García, Antonio; Martín, Juan F; Burroughs, Nigel J; Wellington, Elizabeth M H; Smith, Margaret C M

2012-02-01

Bacteria in the genus Streptomyces are soil-dwelling oligotrophs and important producers of secondary metabolites. Previously, we showed that global messenger RNA expression was subject to a series of metabolic and regulatory switches during the lifetime of a fermentor batch culture of Streptomyces coelicolor M145. Here we analyze the proteome from eight time points from the same fermentor culture and, because phosphate availability is an important regulator of secondary metabolite production, compare this to the proteome of a similar time course from an S. coelicolor mutant, INB201 (ΔphoP), defective in the control of phosphate utilization. The proteomes provide a detailed view of enzymes involved in central carbon and nitrogen metabolism. Trends in protein expression over the time courses were deduced from a protein abundance index, which also revealed the importance of stress pathway proteins in both cultures. As expected, the ΔphoP mutant was deficient in expression of PhoP-dependent genes, and several putatively compensatory metabolic and regulatory pathways for phosphate scavenging were detected. Notably there is a succession of switches that coordinately induce the production of enzymes for five different secondary metabolite biosynthesis pathways over the course of the batch cultures.

Detection of a sudden change of the field time series based on the Lorenz system

PubMed Central

Li, Fang; Shen, BingLu; Yan, PengCheng; Song, Jian; Ma, DeShan

2017-01-01

We conducted an exploratory study of the detection of a sudden change of the field time series based on the numerical solution of the Lorenz system. First, the time when the Lorenz path jumped between the regions on the left and right of the equilibrium point of the Lorenz system was quantitatively marked and the sudden change time of the Lorenz system was obtained. Second, the numerical solution of the Lorenz system was regarded as a vector; thus, this solution could be considered as a vector time series. We transformed the vector time series into a time series using the vector inner product, considering the geometric and topological features of the Lorenz system path. Third, the sudden change of the resulting time series was detected using the sliding t-test method. Comparing the test results with the quantitatively marked time indicated that the method could detect every sudden change of the Lorenz path, thus the method is effective. Finally, we used the method to detect the sudden change of the pressure field time series and temperature field time series, and obtained good results for both series, which indicates that the method can apply to high-dimension vector time series. Mathematically, there is no essential difference between the field time series and vector time series; thus, we provide a new method for the detection of the sudden change of the field time series. PMID:28141832

Single-qubit decoherence under a separable coupling to a random matrix environment

NASA Astrophysics Data System (ADS)

Carrera, M.; Gorin, T.; Seligman, T. H.

2014-08-01

This paper describes the dynamics of a quantum two-level system (qubit) under the influence of an environment modeled by an ensemble of random matrices. In distinction to earlier work, we consider here separable couplings and focus on a regime where the decoherence time is of the same order of magnitude as the environmental Heisenberg time. We derive an analytical expression in the linear response approximation, and study its accuracy by comparison with numerical simulations. We discuss a series of unusual properties, such as purity oscillations, strong signatures of spectral correlations (in the environment Hamiltonian), memory effects, and symmetry-breaking equilibrium states.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.

PubMed

Abduallah, Yasser; Turki, Turki; Byron, Kevin; Du, Zongxuan; Cervantes-Cervantes, Miguel; Wang, Jason T L

2017-01-01

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Kato perturbative expansion in classical mechanics and an explicit expression for the Deprit generator

NASA Astrophysics Data System (ADS)

Nikolaev, A. S.

2015-03-01

We study the structure of the canonical Poincaré-Lindstedt perturbation series in the Deprit operator formalism and establish its connection to the Kato resolvent expansion. A discussion of invariant definitions for averaging and integrating perturbation operators and their canonical identities reveals a regular pattern in the series for the Deprit generator. This regularity is explained using Kato series and the relation of the perturbation operators to the Laurent coefficients for the resolvent of the Liouville operator. This purely canonical approach systematizes the series and leads to an explicit expression for the Deprit generator in any order of the perturbation theory: , where is the partial pseudoinverse of the perturbed Liouville operator. The corresponding Kato series provides a reasonably effective computational algorithm. The canonical connection of the perturbed and unperturbed averaging operators allows describing ambiguities in the generator and transformed Hamiltonian, while Gustavson integrals turn out to be insensitive to the normalization style. We use nonperturbative examples for illustration.

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information.

PubMed

Tuarob, Suppawong; Tucker, Conrad S; Kumara, Soundar; Giles, C Lee; Pincus, Aaron L; Conroy, David E; Ram, Nilam

2017-04-01

It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual's mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population. Furthermore, some individuals may also be unaware of their mental states or may feel uncomfortable to express themselves during the evaluations. Hence, their anomalous mental states could remain undetected for a prolonged period of time. The objective of this work is to demonstrate the ability of using advanced machine learning based approaches to generate mathematical models that predict current and future mental states of an individual. The problem of mental state prediction is transformed into the time series forecasting problem, where an individual is represented as a multivariate time series stream of monitored physical and behavioral attributes. A personalized mathematical model is then automatically generated to capture the dependencies among these attributes, which is used for prediction of mental states for each individual. In particular, we first illustrate the drawbacks of traditional multivariate time series forecasting methodologies such as vector autoregression. Then, we show that such issues could be mitigated by using machine learning regression techniques which are modified for capturing temporal dependencies in time series data. A case study using the data from 150 human participants illustrates that the proposed machine learning based forecasting methods are more suitable for high-dimensional psychological data than the traditional vector autoregressive model in terms of both magnitude of error and directional accuracy. These results not only present a successful usage of machine learning techniques in psychological studies, but also serve as a building block for multiple medical applications that could rely on an automated system to gauge individuals' mental states. Copyright © 2017 Elsevier Inc. All rights reserved.

Multiple Indicator Stationary Time Series Models.

ERIC Educational Resources Information Center

Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

Direct solution for thermal stresses in a nose cap under an arbitrary axisymmetric temperature distribution

NASA Technical Reports Server (NTRS)

Davis, Randall C.

1988-01-01

The design of a nose cap for a hypersonic vehicle is an iterative process requiring a rapid, easy to use and accurate stress analysis. The objective of this paper is to develop such a stress analysis technique from a direct solution of the thermal stress equations for a spherical shell. The nose cap structure is treated as a thin spherical shell with an axisymmetric temperature distribution. The governing differential equations are solved by expressing the stress solution to the thermoelastic equations in terms of a series of derivatives of the Legendre polynomials. The process of finding the coefficients for the series solution in terms of the temperature distribution is generalized by expressing the temperature along the shell and through the thickness as a polynomial in the spherical angle coordinate. Under this generalization the orthogonality property of the Legendre polynomials leads to a sequence of integrals involving powers of the spherical shell coordinate times the derivative of the Legendre polynomials. The coefficients of the temperature polynomial appear outside of these integrals. Thus, the integrals are evaluated only once and their values tabulated for use with any arbitrary polynomial temperature distribution.

Efficient Algorithms for Segmentation of Item-Set Time Series

NASA Astrophysics Data System (ADS)

Chundi, Parvathi; Rosenkrantz, Daniel J.

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Husain, Mainul, E-mail: mainul.husain@hc-sc.gc.ca; Kyjovska, Zdenka O., E-mail: zky@nrcwe.dk; Bourdon-Lacombe, Julie, E-mail: julie.bourdon-lacombe@hc-sc.gc.ca

Inhalation of carbon black nanoparticles (CBNPs) causes pulmonary inflammation; however, time course data to evaluate the detailed evolution of lung inflammatory responses are lacking. Here we establish a time-series of lung inflammatory response to CBNPs. Female C57BL/6 mice were intratracheally instilled with 162 μg CBNPs alongside vehicle controls. Lung tissues were examined 3 h, and 1, 2, 3, 4, 5, 14, and 42 days (d) post-exposure. Global gene expression and pulmonary inflammation were assessed. DNA damage was evaluated in bronchoalveolar lavage (BAL) cells and lung tissue using the comet assay. Increased neutrophil influx was observed at all time-points. DNA strandmore » breaks were increased in BAL cells 3 h post-exposure, and in lung tissues 2–5 d post-exposure. Approximately 2600 genes were differentially expressed (± 1.5 fold; p ≤ 0.05) across all time-points in the lungs of exposed mice. Altered transcript levels were associated with immune-inflammatory response and acute phase response pathways, consistent with the BAL profiles and expression changes found in common respiratory infectious diseases. Genes involved in DNA repair, apoptosis, cell cycle regulation, and muscle contraction were also differentially expressed. Gene expression changes associated with inflammatory response followed a biphasic pattern, with initial changes at 3 h post-exposure declining to base-levels by 3 d, increasing again at 14 d, and then persisting to 42 d post-exposure. Thus, this single CBNP exposure that was equivalent to nine 8-h working days at the current Danish occupational exposure limit induced biphasic inflammatory response in gene expression that lasted until 42 d post-exposure, raising concern over the chronic effects of CBNP exposure. - Highlights: • A single exposure to CBNPs induced expression changes in over 2600 genes in mouse lungs. • Altered genes were associated with immune-inflammatory and acute phase responses. • Several genes were involved in DNA repair, apoptosis, and muscle contraction. • Effects of a single exposure to CBNPs lasted until 42 d post-exposure. • A single exposure to CBNPs induced a biphasic inflammatory response in gene expression.« less

A novel weight determination method for time series data aggregation

NASA Astrophysics Data System (ADS)

Xu, Paiheng; Zhang, Rong; Deng, Yong

2017-09-01

Aggregation in time series is of great importance in time series smoothing, predicting and other time series analysis process, which makes it crucial to address the weights in times series correctly and reasonably. In this paper, a novel method to obtain the weights in time series is proposed, in which we adopt induced ordered weighted aggregation (IOWA) operator and visibility graph averaging (VGA) operator and linearly combine the weights separately generated by the two operator. The IOWA operator is introduced to the weight determination of time series, through which the time decay factor is taken into consideration. The VGA operator is able to generate weights with respect to the degree distribution in the visibility graph constructed from the corresponding time series, which reflects the relative importance of vertices in time series. The proposed method is applied to two practical datasets to illustrate its merits. The aggregation of Construction Cost Index (CCI) demonstrates the ability of proposed method to smooth time series, while the aggregation of The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) illustrate how proposed method maintain the variation tendency of original data.

Characteristics of Hydrogen Sensors Based on Thin Tin Dioxide Films Modified with Gold

NASA Astrophysics Data System (ADS)

Almaev, A. V.; Gaman, V. I.

2017-11-01

Effect of hydrogen in the concentration range from 10 to 2000 ppm on the characteristics of sensors based on thin films of tin dioxide modified with gold (Au/SnO2:Sb, Au) is studied in the thermo-cyclic mode at temperatures from 623 to 773 K and absolute humidity from 2.5 to 20 g/m3. Experimental data are discussed using expressions obtained within the framework of a model that takes into account the presence of three types of adsorbed particles (O¯, OH, and OH¯) on the surface of SnO2 nanocrystals. The characteristics of the sensors based on thin Pt/Pd/SnO2:Sb films (the first series) are compared with those of Au/SnO2:Sb, Au films (the second series). It is found that the degree of dissociation of molecular hydrogen into atoms during adsorption on the sensor under interaction with Au particles on the SnO2 surface is 4 times greater than that under interaction with Pt/Pd particles. The degree of dissociation of H2O molecules into hydrogen atoms and hydroxyl groups in pure moist air on the surface of the sensors of the second series is 1.6 times greater than that for the sensors of the first series. Thus, gold is a more effective stimulator of the dissociation of H2 and H2O molecules than platinum and palladium. A formula is obtained that describes more accurately the dependence of the response of the sensors of both series to the effect of hydrogen on the concentration of this gas and on the temperature of the measuring devices.

Association mining of dependency between time series

NASA Astrophysics Data System (ADS)

Hafez, Alaaeldin

2001-03-01

Time series analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Time series data is a sequence of observations collected over intervals of time. Each time series describes a phenomenon as a function of time. Analysis on time series data includes discovering trends (or patterns) in a time series sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In this paper, we adapt and innovate data mining techniques to analyze time series data. By using data mining techniques, maximal frequent patterns are discovered and used in predicting future sequences or trends, where trends describe the behavior of a sequence. In order to include different types of time series (e.g. irregular and non- systematic), we consider past frequent patterns of the same time sequences (local patterns) and of other dependent time sequences (global patterns). We use the word 'dependent' instead of the word 'similar' for emphasis on real life time series where two time series sequences could be completely different (in values, shapes, etc.), but they still react to the same conditions in a dependent way. In this paper, we propose the Dependence Mining Technique that could be used in predicting time series sequences. The proposed technique consists of three phases: (a) for all time series sequences, generate their trend sequences, (b) discover maximal frequent trend patterns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future time series sequences.

Low-level laser therapy (LLLT; 780 nm) acts differently on mRNA expression of anti- and pro-inflammatory mediators in an experimental model of collagenase-induced tendinitis in rat.

PubMed

Pires, Débora; Xavier, Murilo; Araújo, Tiago; Silva, José Antônio; Aimbire, Flavio; Albertini, Regiane

2011-01-01

Low-level laser therapy (LLLT) has been found to produce anti-inflammatory effects in a variety of disorders. Tendinopathies are directly related to unbalance in expression of pro- and anti-inflammatory cytokines which are responsible by degeneration process of tendinocytes. In the current study, we decided to investigate if LLLT could reduce mRNA expression for TNF-α, IL-1β, IL-6, TGF-β cytokines, and COX-2 enzyme. Forty-two male Wistar rats were divided randomly in seven groups, and tendinitis was induced with a collagenase intratendinea injection. The mRNA expression was evaluated by real-time PCR in 7th and 14th days after tendinitis. LLLT irradiation with wavelength of 780 nm required for 75 s with a dose of 7.7 J/cm(2) was administered in distinct moments: 12 h and 7 days post tendinitis. At the 12 h after tendinitis, the animals were irradiated once in intercalate days until the 7th or 14th day in and them the animals were killed, respectively. In other series, 7 days after tendinitis, the animals were irradiated once in intercalated days until the 14th day and then the animals were killed. LLLT in both acute and chronic phases decreased IL-6, COX-2, and TGF-β expression after tendinitis, respectively, when compared to tendinitis groups: IL-6, COX-2, and TGF-β. The LLLT not altered IL-1β expression in any time, but reduced the TNF-α expression; however, only at chronic phase. We conclude that LLLT administered with this protocol reduces one of features of tendinopathies that is mRNA expression for pro-inflammatory mediators.

On the convergence of local approximations to pseudodifferential operators with applications

NASA Technical Reports Server (NTRS)

Hagstrom, Thomas

1994-01-01

We consider the approximation of a class pseudodifferential operators by sequences of operators which can be expressed as compositions of differential operators and their inverses. We show that the error in such approximations can be bounded in terms of L(1) error in approximating a convolution kernel, and use this fact to develop convergence results. Our main result is a finite time convergence analysis of the Engquist-Majda Pade approximants to the square root of the d'Alembertian. We also show that no spatially local approximation to this operator can be convergent uniformly in time. We propose some temporally local but spatially nonlocal operators with better long time behavior. These are based on Laguerre and exponential series.

Analytical solution of the transient temperature profile in gain medium of passively Q-switched microchip laser.

PubMed

Han, Xiahui; Li, Jianlang

2014-11-01

The transient temperature evolution in the gain medium of a continuous wave (CW) end-pumped passively Q-switched microchip (PQSM) laser is analyzed. By approximating the time-dependent population inversion density as a sawtooth function of time and treating the time-dependent pump absorption of a CW end-pumped PQSM laser as the superposition of an infinite series of short pumping pulses, the analytical expressions of transient temperature evolution and distribution in the gain medium for four- and three-level laser systems, respectively, are given. These analytical solutions are applied to evaluate the transient temperature evolution and distribution in the gain medium of CW end-pumped PQSM Nd:YAG and Yb:YAG lasers.

Updating Landsat time series of surface-reflectance composites and forest change products with new observations

NASA Astrophysics Data System (ADS)

Hermosilla, Txomin; Wulder, Michael A.; White, Joanne C.; Coops, Nicholas C.; Hobart, Geordie W.

2017-12-01

The use of time series satellite data allows for the temporally dense, systematic, transparent, and synoptic capture of land dynamics over time. Subsequent to the opening of the Landsat archive, several time series approaches for characterizing landscape change have been developed, often representing a particular analytical time window. The information richness and widespread utility of these time series data have created a need to maintain the currency of time series information via the addition of new data, as it becomes available. When an existing time series is temporally extended, it is critical that previously generated change information remains consistent, thereby not altering reported change statistics or science outcomes based on that change information. In this research, we investigate the impacts and implications of adding additional years to an existing 29-year annual Landsat time series for forest change. To do so, we undertook a spatially explicit comparison of the 29 overlapping years of a time series representing 1984-2012, with a time series representing 1984-2016. Surface reflectance values, and presence, year, and type of change were compared. We found that the addition of years to extend the time series had minimal effect on the annual surface reflectance composites, with slight band-specific differences (r ≥ 0.1) in the final years of the original time series being updated. The area of stand replacing disturbances and determination of change year are virtually unchanged for the overlapping period between the two time-series products. Over the overlapping temporal period (1984-2012), the total area of change differs by 0.53%, equating to an annual difference in change area of 0.019%. Overall, the spatial and temporal agreement of the changes detected by both time series was 96%. Further, our findings suggest that the entire pre-existing historic time series does not need to be re-processed during the update process. Critically, given the time series change detection and update approach followed here, science outcomes or reports representing one temporal epoch can be considered stable and will not be altered when a time series is updated with newly available data.

pLR: a lentiviral backbone series to stable transduction of bicistronic genes and exchange of promoters.

PubMed

Vargas, José Eduardo; Salton, Gabrielle; Sodré de Castro Laino, Andressa; Pires, Tiago Dalberto; Bonamino, Martin; Lenz, Guido; Delgado-Cañedo, Andrés

2012-11-01

Gene transfer based on lentiviral vectors allow the integration of exogenous genes into the genome of a target cell, turning these vectors into one of the most used methods for stable transgene expression in mammalian cells, in vitro and in vivo. Currently, there are no lentivectors that allow the cloning of different genes to be regulated by different promoters. Also, there are none that permit the analysis of the expression through an IRES (internal ribosome entry site)-- reporter gene system. In this work, we have generated a series of lentivectors containing: (1) a malleable structure to allow the cloning of different target genes in a multicloning site (mcs); (2) unique site to exchange promoters, and (3) IRES followed by one of two reporter genes: eGFP or DsRed. The series of the produced vectors were named pLR (for lentivirus and RSV promoter) and were fairly efficient with a strong fluorescence of the reporter genes in direct transfection and viral transduction experiments. This being said, the pLR series have been found to be powerful biotechnological tools for stable gene transfer and expression. Copyright © 2012 Elsevier Inc. All rights reserved.

Highly comparative time-series analysis: the empirical structure of time series and their methods.

PubMed

Fulcher, Ben D; Little, Max A; Jones, Nick S

2013-06-06

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Highly comparative time-series analysis: the empirical structure of time series and their methods

PubMed Central

Fulcher, Ben D.; Little, Max A.; Jones, Nick S.

2013-01-01

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines. PMID:23554344

A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

ERIC Educational Resources Information Center

Ngan, Chun-Kit

2013-01-01

Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

Fractal analyses reveal independent complexity and predictability of gait

PubMed Central

Dierick, Frédéric; Nivard, Anne-Laure

2017-01-01

Locomotion is a natural task that has been assessed for decades and used as a proxy to highlight impairments of various origins. So far, most studies adopted classical linear analyses of spatio-temporal gait parameters. Here, we use more advanced, yet not less practical, non-linear techniques to analyse gait time series of healthy subjects. We aimed at finding more sensitive indexes related to spatio-temporal gait parameters than those previously used, with the hope to better identify abnormal locomotion. We analysed large-scale stride interval time series and mean step width in 34 participants while altering walking direction (forward vs. backward walking) and with or without galvanic vestibular stimulation. The Hurst exponent α and the Minkowski fractal dimension D were computed and interpreted as indexes expressing predictability and complexity of stride interval time series, respectively. These holistic indexes can easily be interpreted in the framework of optimal movement complexity. We show that α and D accurately capture stride interval changes in function of the experimental condition. Walking forward exhibited maximal complexity (D) and hence, adaptability. In contrast, walking backward and/or stimulation of the vestibular system decreased D. Furthermore, walking backward increased predictability (α) through a more stereotyped pattern of the stride interval and galvanic vestibular stimulation reduced predictability. The present study demonstrates the complementary power of the Hurst exponent and the fractal dimension to improve walking classification. Our developments may have immediate applications in rehabilitation, diagnosis, and classification procedures. PMID:29182659

Hybrid perturbation methods based on statistical time series models

NASA Astrophysics Data System (ADS)

San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario

2016-04-01

In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.

A Review of Subsequence Time Series Clustering

PubMed Central

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

A review of subsequence time series clustering.

PubMed

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

Uncertainty Quantification of Evapotranspiration and Infiltration from Modeling and Historic Time Series at the Savannah River F-Area

NASA Astrophysics Data System (ADS)

Faybishenko, B.; Flach, G. P.

2012-12-01

The objectives of this presentation are: (a) to illustrate the application of Monte Carlo and fuzzy-probabilistic approaches for uncertainty quantification (UQ) in predictions of potential evapotranspiration (PET), actual evapotranspiration (ET), and infiltration (I), using uncertain hydrological or meteorological time series data, and (b) to compare the results of these calculations with those from field measurements at the U.S. Department of Energy Savannah River Site (SRS), near Aiken, South Carolina, USA. The UQ calculations include the evaluation of aleatory (parameter uncertainty) and epistemic (model) uncertainties. The effect of aleatory uncertainty is expressed by assigning the probability distributions of input parameters, using historical monthly averaged data from the meteorological station at the SRS. The combined effect of aleatory and epistemic uncertainties on the UQ of PET, ET, and Iis then expressed by aggregating the results of calculations from multiple models using a p-box and fuzzy numbers. The uncertainty in PETis calculated using the Bair-Robertson, Blaney-Criddle, Caprio, Hargreaves-Samani, Hamon, Jensen-Haise, Linacre, Makkink, Priestly-Taylor, Penman, Penman-Monteith, Thornthwaite, and Turc models. Then, ET is calculated from the modified Budyko model, followed by calculations of I from the water balance equation. We show that probabilistic and fuzzy-probabilistic calculations using multiple models generate the PET, ET, and Idistributions, which are well within the range of field measurements. We also show that a selection of a subset of models can be used to constrain the uncertainty quantification of PET, ET, and I.

Attractor States in Teaching and Learning Processes: A Study of Out-of-School Science Education.

PubMed

Geveke, Carla H; Steenbeek, Henderien W; Doornenbal, Jeannette M; Van Geert, Paul L C

2017-01-01

In order for out-of-school science activities that take place during school hours but outside the school context to be successful, instructors must have sufficient pedagogical content knowledge (PCK) to guarantee high-quality teaching and learning. We argue that PCK is a quality of the instructor-pupil system that is constructed in real-time interaction. When PCK is evident in real-time interaction, we define it as Expressed Pedagogical Content Knowledge (EPCK). The aim of this study is to empirically explore whether EPCK shows a systematic pattern of variation, and if so whether the pattern occurs in recurrent and temporary stable attractor states as predicted in the complex dynamic systems theory. This study concerned nine out-of-school activities in which pupils of upper primary school classes participated. A multivariate coding scheme was used to capture EPCK in real time. A principal component analysis of the time series of all the variables reduced the number of components. A cluster revealed general descriptions of the components across all cases. Cluster analyses of individual cases divided the time series into sequences, revealing High-, Low-, and Non-EPCK states. High-EPCK attractor states emerged at particular moments during activities, rather than being present all the time. Such High-EPCK attractor states were only found in a few cases, namely those where the pupils were prepared for the visit and the instructors were trained.

Multiscale structure of time series revealed by the monotony spectrum.

PubMed

Vamoş, Călin

2017-03-01

Observation of complex systems produces time series with specific dynamics at different time scales. The majority of the existing numerical methods for multiscale analysis first decompose the time series into several simpler components and the multiscale structure is given by the properties of their components. We present a numerical method which describes the multiscale structure of arbitrary time series without decomposing them. It is based on the monotony spectrum defined as the variation of the mean amplitude of the monotonic segments with respect to the mean local time scale during successive averagings of the time series, the local time scales being the durations of the monotonic segments. The maxima of the monotony spectrum indicate the time scales which dominate the variations of the time series. We show that the monotony spectrum can correctly analyze a diversity of artificial time series and can discriminate the existence of deterministic variations at large time scales from the random fluctuations. As an application we analyze the multifractal structure of some hydrological time series.

Analytical solution of the Poisson-Nernst-Planck equations for an electrochemical system close to electroneutrality

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pabst, M., E-mail: M.Pabst@fz-juelich.de

2014-06-14

Single charge densities and the potential are used to describe models of electrochemical systems. These quantities can be calculated by solving a system of time dependent nonlinear coupled partial differential equations, the Poisson-Nernst-Planck equations. Assuming small deviations from the electroneutral equilibrium, the linearized and decoupled equations are solved for a radial symmetric geometry, which represents the interface between a cell and a sensor device. The densities and the potential are expressed by Fourier-Bessels series. The system considered has a ratio between the Debye-length and its geometric dimension on the order of 10{sup −4} so the Fourier-Bessel series can be approximatedmore » by elementary functions. The time development of the system is characterized by two time constants, τ{sub c} and τ{sub g}. The constant τ{sub c} describes the approach to the stationary state of the total charge and the potential. τ{sub c} is several orders of magnitude smaller than the geometry-dependent constant τ{sub g}, which is on the order of 10 ms characterizing the transition to the stationary state of the single ion densities.« less

[Differential gene expression profile in ischemic myocardium of Wistar rats with acute myocardial infarction: the study on gene construction, identification and function].

PubMed

Guo, Chun Yu; Yin, Hui Jun; Jiang, Yue Rong; Xue, Mei; Zhang, Lu; Shi, Da Zhuo

2008-06-18

To construct the differential genes expressed profile in the ischemic myocardium tissue reduced from acute myocardial infarction(AMI), and determine the biological functions of target genes. AMI model was generated by ligation of the left anterior descending coronary artery in Wistar rats. Total RNA was extracted from the normal and the ischemic heart tissues under the ligation point 7 days after the operation. Differential gene expression profiles of the two samples were constructed using Long Serial Analysis of Gene Expression(LongSAGE). Real time fluorescence quantitative PCR was used to verify gene expression profile and to identify the expression of 2 functional genes. The activities of enzymes from functional genes were determined by histochemistry. A total of 15,966 tags were screened from the normal and the ischemic LongSAGE maps. The similarities of the sequences were compared using the BLAST algebra in NCBI and 7,665 novel tags were found. In the ischemic tissue 142 genes were significantly changed compared with those in the normal tissue (P<0.05). These differentially expressed genes represented the proteins which might play important roles in the pathways of oxidation and phosphorylation, ATP synthesis and glycolysis. The partial genes identified by LongSAGE were confirmed using real time fluorescence quantitative PCR. Two genes related to energy metabolism, COX5a and ATP5e, were screened and quantified. Expression of two functional genes down-regulated at their mRNA levels and the activities of correlative functional enzymes decreased compared with those in the normal tissue. AMI causes a series of changes in gene expression, in which the abnormal expression of genes related to energy metabolism could be one of the molecular mechanisms of AMI. The intervention of the expressions of COX5a and ATP5e may be a new target for AMI therapy.

76 FR 6646 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing of Proposed Rule...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-07

... Series, adjusted option series and any options series until the time to expiration for such series is... time to expiration for such series is less than nine months be treated differently. Specifically, under... series until the time to expiration for such series is less than nine months. Accordingly, the...

NCAM (CD56) expression in keratin-producing odontogenic cysts: aberrant expression in KCOT.

PubMed

Vera-Sirera, Beatriz; Forner-Navarro, Leopoldo; Vera-Sempere, Francisco

2015-02-12

To investigate immunohistochemically the expression of neural cell adhesion molecule (NCAM), which has been identified as a signaling receptor with frequent reactivity in ameloblastomas (AB), in a series of keratin-producing odontogenic cysts (KPOCs). Immunohistochemical expression of NCAM, using a monoclonal antibody, was determined in a series of 58 KPOCs comprising 12 orthokeratinized odontogenic cysts (OOCs) and 46 keratocystic odontogenic tumors (KCOTs), corresponding to 40 non-syndromic KCOT (NS-KCOTs) and 6 syndromic KCOT (S-KCOTs), associated with nevic basocellular syndrome (NBCS). NCAM expression was negative in all OOCs, but 36.45% of KCOTs exhibited focal and heterogeneous expression at the basal cell level, as well as in basal budding areas and the basal cells of daughter cysts. The latter two locations were especially applicable to S-KCOTs, with focal NCAM reactivity occurring in 66.66% of cases. Aberrant NCAM expression, in KCOTs but especially in S-KCOTs, together with its immunomorphological location, suggests that this adhesion molecule and signaling receptor plays a role in the pathogenesis of KCOTs, with a probable impact on lesional recurrence.

Complexity quantification of cardiac variability time series using improved sample entropy (I-SampEn).

PubMed

Marwaha, Puneeta; Sunkaria, Ramesh Kumar

2016-09-01

The sample entropy (SampEn) has been widely used to quantify the complexity of RR-interval time series. It is a fact that higher complexity, and hence, entropy is associated with the RR-interval time series of healthy subjects. But, SampEn suffers from the disadvantage that it assigns higher entropy to the randomized surrogate time series as well as to certain pathological time series, which is a misleading observation. This wrong estimation of the complexity of a time series may be due to the fact that the existing SampEn technique updates the threshold value as a function of long-term standard deviation (SD) of a time series. However, time series of certain pathologies exhibits substantial variability in beat-to-beat fluctuations. So the SD of the first order difference (short term SD) of the time series should be considered while updating threshold value, to account for period-to-period variations inherited in a time series. In the present work, improved sample entropy (I-SampEn), a new methodology has been proposed in which threshold value is updated by considering the period-to-period variations of a time series. The I-SampEn technique results in assigning higher entropy value to age-matched healthy subjects than patients suffering atrial fibrillation (AF) and diabetes mellitus (DM). Our results are in agreement with the theory of reduction in complexity of RR-interval time series in patients suffering from chronic cardiovascular and non-cardiovascular diseases.

Phenomenological analysis of medical time series with regular and stochastic components

NASA Astrophysics Data System (ADS)

Timashev, Serge F.; Polyakov, Yuriy S.

2007-06-01

Flicker-Noise Spectroscopy (FNS), a general approach to the extraction and parameterization of resonant and stochastic components contained in medical time series, is presented. The basic idea of FNS is to treat the correlation links present in sequences of different irregularities, such as spikes, "jumps", and discontinuities in derivatives of different orders, on all levels of the spatiotemporal hierarchy of the system under study as main information carriers. The tools to extract and analyze the information are power spectra and difference moments (structural functions), which complement the information of each other. The structural function stochastic component is formed exclusively by "jumps" of the dynamic variable while the power spectrum stochastic component is formed by both spikes and "jumps" on every level of the hierarchy. The information "passport" characteristics that are determined by fitting the derived expressions to the experimental variations for the stochastic components of power spectra and structural functions are interpreted as the correlation times and parameters that describe the rate of "memory loss" on these correlation time intervals for different irregularities. The number of the extracted parameters is determined by the requirements of the problem under study. Application of this approach to the analysis of tremor velocity signals for a Parkinsonian patient is discussed.

(Bis)urea and (Bis)thiourea Inhibitors of Lysine-Specific Demethylase 1 as Epigenetic Modulators

PubMed Central

Sharma, Shiv K.; Wu, Yu; Steinbergs, Nora; Crowley, Michael L.; Hanson, Allison S.; Casero, Robert A.; Woster, Patrick M.

2010-01-01

The recently discovered enzyme lysine-specific demethylase 1 (LSD1) plays an important role in the epigenetic control of gene expression, and aberrant gene silencing secondary to LSD1 over expression is thought to contribute to the development of cancer. We recently reported a series of (bis)guanidines and (bis)biguanides that are potent inhibitors of LSD1, and induce the re-expression of aberrantly silenced tumor suppressor genes in tumor cells in vitro. We now report a series of isosteric ureas and thioureas that are also potent inhibitors of LSD1. These compounds induce increases in methylation at the histone 3 lysine 4 (H3K4) chromatin mark, a specific target of LSD1, in Calu-6 lung carcinoma cells. In addition, these analogues increase cellular levels of secreted frizzle-related proteins (SFRP) 2 and 5, and transcription factor GATA4. These compounds represent an important new series of epigenetic modulators with the potential for use as antitumor agents. PMID:20568780

Generalisation of Gilbert damping and magnetic inertia parameter as a series of higher-order relativistic terms

NASA Astrophysics Data System (ADS)

Mondal, Ritwik; Berritta, Marco; Oppeneer, Peter M.

2018-07-01

The phenomenological Landau–Lifshitz–Gilbert (LLG) equation of motion remains as the cornerstone of contemporary magnetisation dynamics studies, wherein the Gilbert damping parameter has been attributed to first-order relativistic effects. To include magnetic inertial effects the LLG equation has previously been extended with a supplemental inertia term; the arising inertial dynamics has been related to second-order relativistic effects. Here we start from the relativistic Dirac equation and, performing a Foldy–Wouthuysen transformation, derive a generalised Pauli spin Hamiltonian that contains relativistic correction terms to any higher order. Using the Heisenberg equation of spin motion we derive general relativistic expressions for the tensorial Gilbert damping and magnetic inertia parameters, and show that these tensors can be expressed as series of higher-order relativistic correction terms. We further show that, in the case of a harmonic external driving field, these series can be summed and we provide closed analytical expressions for the Gilbert and inertial parameters that are functions of the frequency of the driving field.

Generalisation of Gilbert damping and magnetic inertia parameter as a series of higher-order relativistic terms.

PubMed

Mondal, Ritwik; Berritta, Marco; Oppeneer, Peter M

2018-05-17

The phenomenological Landau-Lifshitz-Gilbert (LLG) equation of motion remains as the cornerstone of contemporary magnetisation dynamics studies, wherein the Gilbert damping parameter has been attributed to first-order relativistic effects. To include magnetic inertial effects the LLG equation has previously been extended with a supplemental inertia term; the arising inertial dynamics has been related to second-order relativistic effects. Here we start from the relativistic Dirac equation and, performing a Foldy-Wouthuysen transformation, derive a generalised Pauli spin Hamiltonian that contains relativistic correction terms to any higher order. Using the Heisenberg equation of spin motion we derive general relativistic expressions for the tensorial Gilbert damping and magnetic inertia parameters, and show that these tensors can be expressed as series of higher-order relativistic correction terms. We further show that, in the case of a harmonic external driving field, these series can be summed and we provide closed analytical expressions for the Gilbert and inertial parameters that are functions of the frequency of the driving field.

[Gene method for inconsistent hydrological frequency calculation. 2: Diagnosis system of hydrological genes and method of hydrological moment genes with inconsistent characters].

PubMed

Xie, Ping; Zhao, Jiang Yan; Wu, Zi Yi; Sang, Yan Fang; Chen, Jie; Li, Bin Bin; Gu, Hai Ting

2018-04-01

The analysis of inconsistent hydrological series is one of the major problems that should be solved for engineering hydrological calculation in changing environment. In this study, the diffe-rences of non-consistency and non-stationarity were analyzed from the perspective of composition of hydrological series. The inconsistent hydrological phenomena were generalized into hydrological processes with inheritance, variability and evolution characteristics or regulations. Furthermore, the hydrological genes were identified following the theory of biological genes, while their inheritance bases and variability bases were determined based on composition of hydrological series under diffe-rent time scales. To identify and test the components of hydrological genes, we constructed a diagnosis system of hydrological genes. With the P-3 distribution as an example, we described the process of construction and expression of the moment genes to illustrate the inheritance, variability and evolution principles of hydrological genes. With the annual minimum 1-month runoff series of Yunjinghong station in Lancangjiang River basin as an example, we verified the feasibility and practicability of hydrological gene theory for the calculation of inconsistent hydrological frequency. The results showed that the method could be used to reveal the evolution of inconsistent hydrological series. Therefore, it provided a new research pathway for engineering hydrological calculation in changing environment and an essential reference for the assessment of water security.

Real-time liquid-crystal atmosphere turbulence simulator with graphic processing unit.

PubMed

Hu, Lifa; Xuan, Li; Li, Dayu; Cao, Zhaoliang; Mu, Quanquan; Liu, Yonggang; Peng, Zenghui; Lu, Xinghai

2009-04-27

To generate time-evolving atmosphere turbulence in real time, a phase-generating method for our liquid-crystal (LC) atmosphere turbulence simulator (ATS) is derived based on the Fourier series (FS) method. A real matrix expression for generating turbulence phases is given and calculated with a graphic processing unit (GPU), the GeForce 8800 Ultra. A liquid crystal on silicon (LCOS) with 256x256 pixels is used as the turbulence simulator. The total time to generate a turbulence phase is about 7.8 ms for calculation and readout with the GPU. A parallel processing method of calculating and sending a picture to the LCOS is used to improve the simulating speed of our LC ATS. Therefore, the real-time turbulence phase-generation frequency of our LC ATS is up to 128 Hz. To our knowledge, it is the highest speed used to generate a turbulence phase in real time.

Preanalytical variables and phosphoepitope expression in FFPE tissue: quantitative epitope assessment after variable cold ischemic time.

PubMed

Vassilakopoulou, Maria; Parisi, Fabio; Siddiqui, Summar; England, Allison M; Zarella, Elizabeth R; Anagnostou, Valsamo; Kluger, Yuval; Hicks, David G; Rimm, David L; Neumeister, Veronique M

2015-03-01

Individualized targeted therapies for cancer patients require accurate and reproducible assessment of biomarkers to be able to plan treatment accordingly. Recent studies have shown highly variable effects of preanalytical variables on gene expression profiling and protein levels of different tissue types. Several publications have described protein degradation of tissue samples as a direct result of delay of formalin fixation of the tissue. Phosphorylated proteins are more labile and epitope degradation can happen within 30 min of cold ischemic time. To address this issue, we evaluated the change in antigenicity of a series of phosphoproteins in paraffin-embedded samples from breast tumors as a function of time to formalin fixation. A tissue microarray consisting of 93 breast cancer specimens with documented time-to-fixation was used to evaluate changes in antigenicity of 12 phosphoepitopes frequently used in research settings as a function of cold ischemic time. Analysis was performed in a quantitative manner using the AQUA technology for quantitative immunofluorescence. For each marker, least squares univariate linear regression was performed and confidence intervals were computed using bootstrapping. The majority of the epitopes tested revealed changes in expression levels with increasing time to formalin fixation. Some phosphorylated proteins, such as phospho-HSP27 and phospho-S6 RP, involved in post-translational modification and stress response pathways increased in expression or phosphorylation levels. Others (like phospho-AKT, phosphor-ERK1/2, phospho-Tyrosine, phospho-MET, and others) are quite labile and loss of antigenicity can be reported within 1-2 h of cold ischemic time. Therefore specimen collection should be closely monitored and subjected to quality control measures to ensure accurate measurement of these epitopes. However, a few phosphoepitopes (like phospho-JAK2 and phospho-ER) are sufficiently robust for routine usage in companion diagnostic testing.

Bridging the Digital Divide between Discrete and Continuous Space-Time Array Data to Enhance Accessibility to and Usability of NASA Earth Sciences Data for the Hydrological Community

NASA Astrophysics Data System (ADS)

Teng, W. L.; Maidment, D. R.; Vollmer, B.; Peters-Lidard, C. D.; Rui, H.; Strub, R.; Whiteaker, T.; Mocko, D. M.; Kirschbaum, D. B.

2012-12-01

A longstanding and significant "Digital Divide" in data representation exists between hydrology and climatology and meteorology. Typically, in hydrology, earth surface features are expressed as discrete spatial objects such as watersheds, river reaches, and point observation sites; and time varying data are contained in time series associated with these spatial objects. Long time histories of data may be associated with a single point or feature in space. In meteorology and climatology, remotely sensed observations and weather and climate model information are expressed as continuous spatial fields, with data sequenced in time from one data file to the next. Hydrology tends to be narrow in space and deep in time, while meteorology and climatology are broad in space and narrow in time. This Divide has been an obstacle, specifically, between the hydrological community, as represented by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) and relevant data sets at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). CUAHSI has developed the Hydrologic Information System (HIS), which is built on international geospatial standards, with one of its aims to bridge the Divide. The opportunity costs of the Divide are high. It has largely prevented the routine access and use of NASA Earth sciences data by the hydrological and, more generally, geospatial community. This presentation describes a recently-begun NASA ACCESS project that addresses the Digital Divide problem. Progress to date is summarized; technical details are provided in a related presentation (Rui et al., Data Reorganization for Optimal Time Series Data Access, Analysis, and Visualization, IN016). Building on prior prototype efforts with EPA BASINS (Better Assessment Science Integrating point and Nonpoint Sources) and CUAHSI HIS, this project focuses on the following approaches to the problems of data discovery, access, and use: (1) Link HIS and GES DISC ontologies to facilitate data service registration in HIS catalog; (2) harvest NASA ECHO catalog with OpenSearch to generalize the solution beyond GES DISC; (3) develop HIS WaterOneFlow Web services for GES DISC data in OGC-compliant WaterML 2.0; (4) reorganize NASA data (land surface model outputs, satellite precipitation and soil moisture data) for optimal access as time series; (5) enhance HIS HydroDesktop client to better handle NASA data; and (6) develop hydrological use cases to guide implementation, and serve as metric for usefulness, of project technologies. This project should significantly extend NASA Earth sciences data to the large and important hydrological user community that has been, heretofore, mostly unable to easily access and use NASA data.

pySAPC, a python package for sparse affinity propagation clustering: Application to odontogenesis whole genome time series gene-expression data.

PubMed

Cao, Huojun; Amendt, Brad A

2016-11-01

Developmental dental anomalies are common forms of congenital defects. The molecular mechanisms of dental anomalies are poorly understood. Systematic approaches such as clustering genes based on similar expression patterns could identify novel genes involved in dental anomalies and provide a framework for understanding molecular regulatory mechanisms of these genes during tooth development (odontogenesis). A python package (pySAPC) of sparse affinity propagation clustering algorithm for large datasets was developed. Whole genome pair-wise similarity was calculated based on expression pattern similarity based on 45 microarrays of several stages during odontogenesis. pySAPC identified 743 gene clusters based on expression pattern similarity during mouse tooth development. Three clusters are significantly enriched for genes associated with dental anomalies (with FDR <0.1). The three clusters of genes have distinct expression patterns during odontogenesis. Clustering genes based on similar expression profiles recovered several known regulatory relationships for genes involved in odontogenesis, as well as many novel genes that may be involved with the same genetic pathways as genes that have already been shown to contribute to dental defects. By using sparse similarity matrix, pySAPC use much less memory and CPU time compared with the original affinity propagation program that uses a full similarity matrix. This python package will be useful for many applications where dataset(s) are too large to use full similarity matrix. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016. Published by Elsevier B.V.

Analysis of gene expression in a developmental context emphasizes distinct biological leitmotifs in human cancers

PubMed Central

Naxerova, Kamila; Bult, Carol J; Peaston, Anne; Fancher, Karen; Knowles, Barbara B; Kasif, Simon; Kohane, Isaac S

2008-01-01

Background In recent years, the molecular underpinnings of the long-observed resemblance between neoplastic and immature tissue have begun to emerge. Genome-wide transcriptional profiling has revealed similar gene expression signatures in several tumor types and early developmental stages of their tissue of origin. However, it remains unclear whether such a relationship is a universal feature of malignancy, whether heterogeneities exist in the developmental component of different tumor types and to which degree the resemblance between cancer and development is a tissue-specific phenomenon. Results We defined a developmental landscape by summarizing the main features of ten developmental time courses and projected gene expression from a variety of human tumor types onto this landscape. This comparison demonstrates a clear imprint of developmental gene expression in a wide range of tumors and with respect to different, even non-cognate developmental backgrounds. Our analysis reveals three classes of cancers with developmentally distinct transcriptional patterns. We characterize the biological processes dominating these classes and validate the class distinction with respect to a new time series of murine embryonic lung development. Finally, we identify a set of genes that are upregulated in most cancers and we show that this signature is active in early development. Conclusion This systematic and quantitative overview of the relationship between the neoplastic and developmental transcriptome spanning dozens of tissues provides a reliable outline of global trends in cancer gene expression, reveals potentially clinically relevant differences in the gene expression of different cancer types and represents a reference framework for interpretation of smaller-scale functional studies. PMID:18611264

Molecular alterations in tumorigenic human bronchial and breast epithelial cells induced by high let radiation

NASA Astrophysics Data System (ADS)

Hei, T. K.; Zhao, Y. L.; Roy, D.; Piao, C. Q.; Calaf, G.; Hall, E. J.

Carcinogenesis is a multi-stage process with sequence of genetic events governing the phenotypic expression of a series of transformation steps leading to the development of metastatic cancer. In the present study, immortalized human bronchial (BEP2D) and breast (MCF-10F) cells were irradiated with graded doses of either 150 keV/μm alpha particles or 1 GeV/nucleon 56Fe ions. Transformed cells developed through a series of successive steps before becoming tumorigenic in nude mice. Cell fusion studies indicated that radiation-induced tumorigenic phenotype in BEP2D cells could be completely suppressed by fusion with non-tumorigenic BEP2D cells. The differential expressions of known genes between tumorigenic bronchial and breast cells induced by alpha particles and their respective control cultures were compared using cDNA expression array. Among the 11 genes identified to be differentially expressed in BEP2D cells, three ( DCC, DNA-PK and p21 CIPI) were shown to be consistently down-regulated by 2 to 4 fold in all the 5 tumor cell lines examined. In contrast, their expressions in the fusion cell lines were comparable to control BEP2D cells. Similarly, expression levels of a series of genes were found to be altered in a step-wise manner among tumorigenic MCF-10F cells. The results are highly suggestive that functional alterations of these genes may be causally related to the carcinogenic process.

Human RON receptor tyrosine kinase induces complete epithelial-to-mesenchymal transition but causes cellular senescence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cote, Marceline; Miller, A. Dusty; Liu, Shan-Lu

2007-08-17

The RON receptor tyrosine kinase is a member of the MET proto-oncogene family and is important for cell proliferation, differentiation, and cancer development. Here, we created a series of Madin-Darby canine kidney (MDCK) epithelial cell clones that express different levels of RON, and have investigated their biological properties. While low levels of RON correlated with little morphological change in MDCK cells, high levels of RON expression constitutively led to morphological scattering or complete and stabilized epithelial-to-mesenchymal transition (EMT). Unexpectedly, MDCK clones expressing higher levels of RON exhibited retarded proliferation and senescence, despite increased motility and invasiveness. RON was constitutively tyrosine-phosphorylatedmore » in MDCK cells expressing high levels of RON and undergoing EMT, and the MAPK signaling pathway was activated. This study reveals for the first time that RON alone is sufficient to induce complete and stabilized EMT in MDCK cells, and overexpression of RON does not cause cell transformation but rather induces cell cycle arrest and senescence, leading to impaired cell proliferation.« less

Taspine isolated from Radix et Rhizoma Leonticis inhibits growth of human umbilical vein endothelial cell (HUVEC) by inducing its apoptosis.

PubMed

Zhang, Yanmin; He, Langchong; Zhou, Yali

2008-01-01

The present study was to evaluate the effects of taspine isolated from Radix et Rhizoma Leonticsi on the growth and apoptosis of human umbilical vein endothelial cell (HUVEC) line by MTT and flow cytometer, respectively. At the same time, a series of changes were observed in HUVEC treated by taspine, including microstructure, protein expression of bax, bcl-2 and VEGF. The change of microstructure was observed by transmission electron microscope (TEM). The protein expression of bax and bcl-2 was detected by immunohistochemistry (IHC), and VEGF protein secreted was determined by enzyme-linked immunosorbent assay (ELISA). The results showed taspine could inhibit growth and induce apoptosis of HUVEC in a dose-dependent manner. Cell cycle was significantly stopped at the S phase. Under electronic microscope, the morphology of HUVEC treated with taspine showed nuclear karyopycnosis, chromatin agglutination and typical apoptotic body. Bcl-2 and VEGF expressions were decreased and bax expression was increased. All these results demonstrate that taspine has an inhibitory effect on growth of HUVEC and can induce its apoptosis.

Coordinate changes in gene expression and triacylglycerol composition in the developing seeds of oilseed rape (Brassica napus) and turnip rape (Brassica rapa).

PubMed

Vuorinen, Anssi L; Kalpio, Marika; Linderborg, Kaisa M; Kortesniemi, Maaria; Lehto, Kirsi; Niemi, Jarmo; Yang, Baoru; Kallio, Heikki P

2014-02-15

Crop production for vegetable oil in the northern latitudes utilises oilseed rape (Brassica napus subsp. oleifera) and turnip rape (B. rapa subsp. oleifera), having similar oil compositions. The oil consists mostly of triacylglycerols, which are synthesised during seed development. In this study, we characterised the oil composition and the expression levels of genes involved in triacylglycerol biosynthesis in the developing seeds in optimal, low temperature (15 °C) and short day (12-h day length) conditions. Gene expression levels of several genes were altered during seed development. Low temperature and short day treatments increased the level of 9,12,15-octadecatrienoic acid (18:3n-3) in turnip rape and short day treatment decreased the total oil content in both species. This study gives a novel view on seed oil biosynthesis under different growth conditions, bringing together gene expression levels of the triacylglycerol biosynthesis pathway and oil composition over a time series in two related oilseed species. Copyright © 2013 Elsevier Ltd. All rights reserved.

Discrimination and Imitation of Facial Expressions by Neonates.

ERIC Educational Resources Information Center

Field, Tiffany

Findings of a series of studies on individual differences and maturational changes in expressivity at the neonatal stage and during early infancy are reported. Research results indicate that newborns are able to discriminate and imitate the basic emotional expressions: happy, sad, and surprised. Results show widened infant lips when the happy…

Adult Perceptions of Positive and Negative Infant Emotional Expressions

ERIC Educational Resources Information Center

Bolzani Dinehart, Laura H.; Messinger, Daniel S.; Acosta, Susan I.; Cassel, Tricia; Ambadar, Zara; Cohn, Jeffrey

2005-01-01

Adults' perceptions provide information about the emotional meaning of infant facial expressions. This study asks whether similar facial movements influence adult perceptions of emotional intensity in both infant positive (smile) and negative (cry face) facial expressions. Ninety-five college students rated a series of naturally occurring and…

Conductor and Ensemble Performance Expressivity and State Festival Ratings

ERIC Educational Resources Information Center

Price, Harry E.; Chang, E. Christina

2005-01-01

This study is the second in a series examining the relationship between conducting and ensemble performance. The purpose was to further examine the associations among conductor, ensemble performance expressivity, and festival ratings. Participants were asked to rate the expressivity of video-only conducting and parallel audio-only excerpts from a…

Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks

PubMed Central

Zhou, Renjie; Yang, Chen; Wan, Jian; Zhang, Wei; Guan, Bo; Xiong, Naixue

2017-01-01

Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques. PMID:28383496

Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks.

PubMed

Zhou, Renjie; Yang, Chen; Wan, Jian; Zhang, Wei; Guan, Bo; Xiong, Naixue

2017-04-06

Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques.

Empirical method to measure stochasticity and multifractality in nonlinear time series

NASA Astrophysics Data System (ADS)

Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping

2013-12-01

An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

Selection of reference genes for quantitative real-time PCR normalization in Panax ginseng at different stages of growth and in different organs.

PubMed

Liu, Jing; Wang, Qun; Sun, Minying; Zhu, Linlin; Yang, Michael; Zhao, Yu

2014-01-01

Quantitative real-time reverse transcription PCR (qRT-PCR) has become a widely used method for gene expression analysis; however, its data interpretation largely depends on the stability of reference genes. The transcriptomics of Panax ginseng, one of the most popular and traditional ingredients used in Chinese medicines, is increasingly being studied. Furthermore, it is vital to establish a series of reliable reference genes when qRT-PCR is used to assess the gene expression profile of ginseng. In this study, we screened out candidate reference genes for ginseng using gene expression data generated by a high-throughput sequencing platform. Based on the statistical tests, 20 reference genes (10 traditional housekeeping genes and 10 novel genes) were selected. These genes were tested for the normalization of expression levels in five growth stages and three distinct plant organs of ginseng by qPCR. These genes were subsequently ranked and compared according to the stability of their expressions using geNorm, NormFinder, and BestKeeper computational programs. Although the best reference genes were found to vary across different samples, CYP and EF-1α were the most stable genes amongst all samples. GAPDH/30S RPS20, CYP/60S RPL13 and CYP/QCR were the optimum pair of reference genes in the roots, stems, and leaves. CYP/60S RPL13, CYP/eIF-5A, aTUB/V-ATP, eIF-5A/SAR1, and aTUB/pol IIa were the most stably expressed combinations in each of the five developmental stages. Our study serves as a foundation for developing an accurate method of qRT-PCR and will benefit future studies on gene expression profiles of Panax Ginseng.

Embracing heterothermic diversity: non-stationary waveform analysis of temperature variation in endotherms.

PubMed

Levesque, Danielle L; Menzies, Allyson K; Landry-Cuerrier, Manuelle; Larocque, Guillaume; Humphries, Murray M

2017-07-01

Recent research is revealing incredible diversity in the thermoregulatory patterns of wild and captive endotherms. As a result of these findings, classic thermoregulatory categories of 'homeothermy', 'daily heterothermy', and 'hibernation' are becoming harder to delineate, impeding our understanding of the physiological and evolutionary significance of variation within and around these categories. However, we lack a generalized analytical approach for evaluating and comparing the complex and diversified nature of the full breadth of heterothermy expressed by individuals, populations, and species. Here we propose a new approach that decomposes body temperature time series into three inherent properties-waveform, amplitude, and period-using a non-stationary technique that accommodates the temporal variability of body temperature patterns. This approach quantifies circadian and seasonal variation in thermoregulatory patterns, and uses the distribution of observed thermoregulatory patterns as a basis for intra- and inter-specific comparisons. We analyse body temperature time series from multiple species, including classical hibernators, tropical heterotherms, and homeotherms, to highlight the approach's general usefulness and the major axes of thermoregulatory variation that it reveals.

Boolean dynamics of genetic regulatory networks inferred from microarray time series data

DOE PAGES

Martin, Shawn; Zhang, Zhaoduo; Martino, Anthony; ...

2007-01-31

Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this paper we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our methodmore » first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation–inhibition networks to match the discretized data. In conclusion, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics.« less

Direct Estimation of Optical Parameters From Photoacoustic Time Series in Quantitative Photoacoustic Tomography.

PubMed

Pulkkinen, Aki; Cox, Ben T; Arridge, Simon R; Goh, Hwan; Kaipio, Jari P; Tarvainen, Tanja

2016-11-01

Estimation of optical absorption and scattering of a target is an inverse problem associated with quantitative photoacoustic tomography. Conventionally, the problem is expressed as two folded. First, images of initial pressure distribution created by absorption of a light pulse are formed based on acoustic boundary measurements. Then, the optical properties are determined based on these photoacoustic images. The optical stage of the inverse problem can thus suffer from, for example, artefacts caused by the acoustic stage. These could be caused by imperfections in the acoustic measurement setting, of which an example is a limited view acoustic measurement geometry. In this work, the forward model of quantitative photoacoustic tomography is treated as a coupled acoustic and optical model and the inverse problem is solved by using a Bayesian approach. Spatial distribution of the optical properties of the imaged target are estimated directly from the photoacoustic time series in varying acoustic detection and optical illumination configurations. It is numerically demonstrated, that estimation of optical properties of the imaged target is feasible in limited view acoustic detection setting.

Kinetics of microwave assisted extraction of pectin from Balinese orange peel using Pseudo-homogeneous model

NASA Astrophysics Data System (ADS)

Megawati, Wulansarie, Ria; Faiz, Merisa Bestari; Adi, Susatyo; Sammadikun, Waliyuddin

2018-03-01

The objective of this work was to study the homogeneous kinetics of pectin extraction of Balinese orange peel conducted using Microwave Assisted Extraction (MAE). The experimental data showed that the power increases (180 to 600 W), so that the extraction yield of pectin also increases (12.2 to 30.6 % w/w). Moreover, the extraction time is longer (10, 15, and 20 min) the yield of pectin increases (8.8, 20.2, and 40.5). At time after of 20 min (25 and 30 min), the yield starts to decrease (36.6 and 22.9). This phenomena shows pectin degradation. Therefore, pectin extraction is a series reaction, i.e. extraction and degradation. The calculation result showed that pseudo series homogeneous model can quantitatively describe the extraction kinetics. The kinetic constants can be expressed by Arrhenius equation with the frequency factors of 1.58 × 105 and 2.29 × 105 1/min, while the activation energies are 64,350 and 56,571 J/mole for extraction and degradation, respectively.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

PubMed

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach

PubMed Central

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Introduction Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. Aim The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Methods Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate – adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Results Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. Conclusion To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research. PMID:26080057

Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

PubMed

Henriques, Teresa S; Mariani, Sara; Burykin, Anton; Rodrigues, Filipa; Silva, Tiago F; Goldberger, Ary L

2016-02-09

Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

Kapteyn series arising in radiation problems

NASA Astrophysics Data System (ADS)

Lerche, I.; Tautz, R. C.

2008-01-01

In discussing radiation from multiple point charges or magnetic dipoles, moving in circles or ellipses, a variety of Kapteyn series of the second kind arises. Some of the series have been known in closed form for a hundred years or more, others appear not to be available to analytic persuasion. This paper shows how 12 such generic series can be developed to produce either closed analytic expressions or integrals that are not analytically tractable. In addition, the method presented here may be of benefit when one has other Kapteyn series of the second kind to consider, thereby providing an additional reason to consider such series anew.

Degree-Pruning Dynamic Programming Approaches to Central Time Series Minimizing Dynamic Time Warping Distance.

PubMed

Sun, Tao; Liu, Hongbo; Yu, Hong; Chen, C L Philip

2016-06-28

The central time series crystallizes the common patterns of the set it represents. In this paper, we propose a global constrained degree-pruning dynamic programming (g(dp)²) approach to obtain the central time series through minimizing dynamic time warping (DTW) distance between two time series. The DTW matching path theory with global constraints is proved theoretically for our degree-pruning strategy, which is helpful to reduce the time complexity and computational cost. Our approach can achieve the optimal solution between two time series. An approximate method to the central time series of multiple time series [called as m_g(dp)²] is presented based on DTW barycenter averaging and our g(dp)² approach by considering hierarchically merging strategy. As illustrated by the experimental results, our approaches provide better within-group sum of squares and robustness than other relevant algorithms.

Analysis of temporal decay of diffuse broadband sound fields in enclosures by decomposition in powers of an absorption parameter

NASA Astrophysics Data System (ADS)

Bliss, Donald; Franzoni, Linda; Rouse, Jerry; Manning, Ben

2005-09-01

An analysis method for time-dependent broadband diffuse sound fields in enclosures is described. Beginning with a formulation utilizing time-dependent broadband intensity boundary sources, the strength of these wall sources is expanded in a series in powers of an absorption parameter, thereby giving a separate boundary integral problem for each power. The temporal behavior is characterized by a Taylor expansion in the delay time for a source to influence an evaluation point. The lowest-order problem has a uniform interior field proportional to the reciprocal of the absorption parameter, as expected, and exhibits relatively slow exponential decay. The next-order problem gives a mean-square pressure distribution that is independent of the absorption parameter and is primarily responsible for the spatial variation of the reverberant field. This problem, which is driven by input sources and the lowest-order reverberant field, depends on source location and the spatial distribution of absorption. Additional problems proceed at integer powers of the absorption parameter, but are essentially higher-order corrections to the spatial variation. Temporal behavior is expressed in terms of an eigenvalue problem, with boundary source strength distributions expressed as eigenmodes. Solutions exhibit rapid short-time spatial redistribution followed by long-time decay of a predominant spatial mode.

Modeling and validation of autoinducer-mediated bacterial gene expression in microfluidic environments

PubMed Central

Austin, Caitlin M.; Stoy, William; Su, Peter; Harber, Marie C.; Bardill, J. Patrick; Hammer, Brian K.; Forest, Craig R.

2014-01-01

Biosensors exploiting communication within genetically engineered bacteria are becoming increasingly important for monitoring environmental changes. Currently, there are a variety of mathematical models for understanding and predicting how genetically engineered bacteria respond to molecular stimuli in these environments, but as sensors have miniaturized towards microfluidics and are subjected to complex time-varying inputs, the shortcomings of these models have become apparent. The effects of microfluidic environments such as low oxygen concentration, increased biofilm encapsulation, diffusion limited molecular distribution, and higher population densities strongly affect rate constants for gene expression not accounted for in previous models. We report a mathematical model that accurately predicts the biological response of the autoinducer N-acyl homoserine lactone-mediated green fluorescent protein expression in reporter bacteria in microfluidic environments by accommodating these rate constants. This generalized mass action model considers a chain of biomolecular events from input autoinducer chemical to fluorescent protein expression through a series of six chemical species. We have validated this model against experimental data from our own apparatus as well as prior published experimental results. Results indicate accurate prediction of dynamics (e.g., 14% peak time error from a pulse input) and with reduced mean-squared error with pulse or step inputs for a range of concentrations (10 μM–30 μM). This model can help advance the design of genetically engineered bacteria sensors and molecular communication devices. PMID:25379076

Nonlinear effects in the time measurement device based on surface acoustic wave filter excitation.

PubMed

Prochazka, Ivan; Panek, Petr

2009-07-01

A transversal surface acoustic wave filter has been used as a time interpolator in a time interval measurement device. We are presenting the experiments and results of an analysis of the nonlinear effects in such a time interpolator. The analysis shows that the nonlinear distortion in the time interpolator circuits causes a deterministic measurement error which can be understood as the time interpolation nonlinearity. The dependence of this error on time of the measured events can be expressed as a sparse Fourier series thus it usually oscillates very quickly in comparison to the clock period. The theoretical model is in good agreement with experiments carried out on an experimental two-channel timing system. Using highly linear amplifiers in the time interpolator and adjusting the filter excitation level to the optimum, we have achieved the interpolation nonlinearity below 0.2 ps. The overall single-shot precision of the experimental timing device is 0.9 ps rms in each channel.

From Networks to Time Series

NASA Astrophysics Data System (ADS)

Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

2012-10-01

In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

76 FR 14111 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-15

... Options Series, adjusted option series and any options series until the time to expiration for such series... time to expiration for such series is less than nine months be treated differently. Specifically, under... until the time to expiration for such series is less than nine months. Accordingly, the requirement to...

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

PubMed Central

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

PubMed

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Prevalence of MLH1 constitutional epimutations as a cause of Lynch syndrome in unselected versus selected consecutive series of patients with colorectal cancer.

PubMed

Castillejo, Adela; Hernández-Illán, Eva; Rodriguez-Soler, María; Pérez-Carbonell, Lucía; Egoavil, Cecilia; Barberá, Victor M; Castillejo, María-Isabel; Guarinos, Carla; Martínez-de-Dueñas, Eduardo; Juan, María-Jose; Sánchez-Heras, Ana-Beatriz; García-Casado, Zaida; Ruiz-Ponte, Clara; Brea-Fernández, Alejandro; Juárez, Miriam; Bujanda, Luis; Clofent, Juan; Llor, Xavier; Andreu, Montserrat; Castells, Antoni; Carracedo, Angel; Alenda, Cristina; Payá, Artemio; Jover, Rodrigo; Soto, José-Luis

2015-07-01

The prevalence of MLH1 constitutional epimutations in the general population is unknown. We sought to analyse the prevalence of MLH1 constitutional epimutations in unselected and selected series of patients with colorectal cancer (CRC). Patients with diagnoses of CRC (n=2123) were included in the unselected group. For comparison, a group of 847 selected patients with CRC who fulfilled the revised Bethesda guidelines (rBG) were also included. Somatic and constitutional MLH1 methylation was assayed via methylation-specific multiplex ligation-dependent probe amplification of cases lacking MLH1 expression. Germline alterations in mismatch-repair (MMR) genes were assessed via Sanger sequencing and methylation-specific multiplex ligation-dependent probe amplification. Loss of MLH1 expression occurred in 5.5% of the unselected series and 12.5% of the selected series (p<0.0001). No constitutional epimutations in MLH1 were detected in the unselected population (0/62); five cases from the selected series were positive for MLH1 epimutations (15.6%, 5/32; p=0.004). Our results suggest a negligible prevalence of MLH1 constitutional epimutations in unselected cases of CRC. Therefore, MLH1 constitutional epimutation analysis should be conducted only for patients who fulfil the rBG and who lack MLH1 expression with methylated MLH1. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

High TRAF6 Expression Is Associated With Esophageal Carcinoma Recurrence and Prompts Cancer Cell Invasion.

PubMed

Liu, Xinyang; Wang, Zhichao; Zhang, Guoliang; Zhu, Qikun; Zeng, Hui; Wang, Tao; Gao, Feng; Qi, Zhan; Zhang, Jinwen; Wang, Rui

2017-04-14

Esophageal cancer is one of the most common types of cancer, and it has a poor prognosis. The molecular mechanisms of esophageal cancer progression remain largely unknown. In this study, we aimed to investigate the clinical significance and biological function of tumor necrosis factor receptor-associated factor 6 (TRAF6) in esophageal cancer. Expression of TRAF6 in esophageal cancer was examined, and its correlation with clinicopathological factors and patient prognosis was analyzed. A series of functional and mechanism assays were performed to further investigate the function and underlying mechanisms in esophageal cancer. Expression of TRAF6 was highly elevated in esophageal cancer tissues, and patients with high TRAF6 expression have a significantly shorter survival time than those with low TRAF6 expression. Furthermore, loss-of-function experiments showed that knockdown of TRAF6 significantly reduced the migration and invasion abilities of esophageal cancer cells. Moreover, the pro-oncogenic effects of TRAF6 in esophageal cancer were mediated by the upregulation of AEP and MMP2. Altogether, our data suggest that high expression of TRAF6 is significant for esophageal cancer progression, and TRAF6 indicates poor prognosis in esophageal cancer patients, which might be a novel prognostic biomarker or potential therapeutic target in esophageal cancer.

Visibility Graph Based Time Series Analysis.

PubMed

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Tracking Spatial and Temporal Changes in Microbial Metabolic Potential and Gene Expression Patterns Across Geochemical Gradients at Axial Seamount

NASA Astrophysics Data System (ADS)

Fortunato, C. S.; Butterfield, D. A.; Larson, B.; Algar, C. K.; Huber, J. A.

2016-12-01

Microbial communities living both near and within the subseafloor are important players in the biogeochemical cycling of the deep ocean. To better understand the metabolic and gene expression patterns of these understudied communities, we collected low-temperature diffuse fluids for metagenomic, metatranscriptomic, and geochemical analyses from Axial Seamount, an active submarine volcano off the coast of Oregon, USA in 2013-2015. In April of 2015 Axial Seamount erupted along its north rift, five months before the 2015 samples were collected. This study thus provides both spatial and temporal analysis of subseafloor microbial communities pre and post eruption. The time series for this study focused on three vents at the south end of the caldera: Anemone, Marker 33, and Marker 113. Chemistry data shows that at each vent there are different geochemical conditions and thus a potentially different microbial metabolic profile. Anemone has the most oxidizing conditions and the highest abundance and expression of sulfur oxidation genes, attributed to both SUP05 and Epsilonproteobacteria. The most reducing conditions were observed at Marker 113, the site with the lowest oxygen concentration and where methanogenesis was the dominant metabolism, with 18.5% of all annotated transcripts attributed to methanogenesis. Although individual vents were metabolically distinct, there was very little variation in the overall taxonomic and metabolic profiles of each vent across years, even after the 2015 eruption. A diffuse fluid sample taken from the North Rift Zone post eruption showed similar community taxonomy to both Anemone and Marker 33; analyses of the metabolic potential and gene expression at this site is ongoing and will act as a comparison between the communities of the time series vents and those that were closer to the eruption site. Together, these chemical and `omic datasets reveal a dynamic microbial community at each vent, taxonomically diverse and involved in a wide array of biogeochemical transformations. Results are being used to model the functional dynamics and fluxes of vent communities to more closely link microbiological productivity at hydrothermal systems to deep-sea biogeochemical processes and will be also used to inform future projects using instrumentation on the cabled array at Axial Seamount.

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

PubMed

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

PubMed

Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

2005-04-15

The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

Zero-crossing statistics for non-Markovian time series.

PubMed

Nyberg, Markus; Lizana, Ludvig; Ambjörnsson, Tobias

2018-03-01

In applications spanning from image analysis and speech recognition to energy dissipation in turbulence and time-to failure of fatigued materials, researchers and engineers want to calculate how often a stochastic observable crosses a specific level, such as zero. At first glance this problem looks simple, but it is in fact theoretically very challenging, and therefore few exact results exist. One exception is the celebrated Rice formula that gives the mean number of zero crossings in a fixed time interval of a zero-mean Gaussian stationary process. In this study we use the so-called independent interval approximation to go beyond Rice's result and derive analytic expressions for all higher-order zero-crossing cumulants and moments. Our results agree well with simulations for the non-Markovian autoregressive model.

Zero-crossing statistics for non-Markovian time series

NASA Astrophysics Data System (ADS)

Nyberg, Markus; Lizana, Ludvig; Ambjörnsson, Tobias

2018-03-01

In applications spanning from image analysis and speech recognition to energy dissipation in turbulence and time-to failure of fatigued materials, researchers and engineers want to calculate how often a stochastic observable crosses a specific level, such as zero. At first glance this problem looks simple, but it is in fact theoretically very challenging, and therefore few exact results exist. One exception is the celebrated Rice formula that gives the mean number of zero crossings in a fixed time interval of a zero-mean Gaussian stationary process. In this study we use the so-called independent interval approximation to go beyond Rice's result and derive analytic expressions for all higher-order zero-crossing cumulants and moments. Our results agree well with simulations for the non-Markovian autoregressive model.

Analysis of Nonstationary Time Series for Biological Rhythms Research.

PubMed

Leise, Tanya L

2017-06-01

This article is part of a Journal of Biological Rhythms series exploring analysis and statistics topics relevant to researchers in biological rhythms and sleep research. The goal is to provide an overview of the most common issues that arise in the analysis and interpretation of data in these fields. In this article on time series analysis for biological rhythms, we describe some methods for assessing the rhythmic properties of time series, including tests of whether a time series is indeed rhythmic. Because biological rhythms can exhibit significant fluctuations in their period, phase, and amplitude, their analysis may require methods appropriate for nonstationary time series, such as wavelet transforms, which can measure how these rhythmic parameters change over time. We illustrate these methods using simulated and real time series.

OLYMPUS: an automated hybrid clustering method in time series gene expression. Case study: host response after Influenza A (H1N1) infection.

PubMed

Dimitrakopoulou, Konstantina; Vrahatis, Aristidis G; Wilk, Esther; Tsakalidis, Athanasios K; Bezerianos, Anastasios

2013-09-01

The increasing flow of short time series microarray experiments for the study of dynamic cellular processes poses the need for efficient clustering tools. These tools must deal with three primary issues: first, to consider the multi-functionality of genes; second, to evaluate the similarity of the relative change of amplitude in the time domain rather than the absolute values; third, to cope with the constraints of conventional clustering algorithms such as the assignment of the appropriate cluster number. To address these, we propose OLYMPUS, a novel unsupervised clustering algorithm that integrates Differential Evolution (DE) method into Fuzzy Short Time Series (FSTS) algorithm with the scope to utilize efficiently the information of population of the first and enhance the performance of the latter. Our hybrid approach provides sets of genes that enable the deciphering of distinct phases in dynamic cellular processes. We proved the efficiency of OLYMPUS on synthetic as well as on experimental data. The discriminative power of OLYMPUS provided clusters, which refined the so far perspective of the dynamics of host response mechanisms to Influenza A (H1N1). Our kinetic model sets a timeline for several pathways and cell populations, implicated to participate in host response; yet no timeline was assigned to them (e.g. cell cycle, homeostasis). Regarding the activity of B cells, our approach revealed that some antibody-related mechanisms remain activated until day 60 post infection. The Matlab codes for implementing OLYMPUS, as well as example datasets, are freely accessible via the Web (http://biosignal.med.upatras.gr/wordpress/biosignal/). Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

EMC: Air Quality Forecast Home page

Science.gov Websites

archive NAM Verification Meteorology Error Time Series EMC NAM Spatial Maps Real Time Mesoscale Analysis Precipitation verification NAQFC VERIFICATION CMAQ Ozone & PM Error Time Series AOD Error Time Series HYSPLIT Smoke forecasts vs GASP satellite Dust and Smoke Error Time Series HYSPLIT WCOSS Upgrade (July

Rembrandt's late self-portraits: psychological and medical aspects.

PubMed

Marcus, Esther-Lee; Clarfield, A Mark

2002-01-01

The Dutch painter Rembrandt (1606-1669) left behind the largest series of self-portraits in the history of art. These paintings were produced over a period of time from age 22 years until just a few months before Rembrandt's death at age 63. This series gives us a unique opportunity to explore the development, maturity, and aging of the artist. The changes in Rembrandt's face and expression from one self-portrait to the next may be attributable to any combination of the following factors: normal aging changes, modifications and developments of his artistic style, alterations in the way he viewed himself, and changes in the way Rembrandt wanted us to see him. In addition, the modifications may be attributed in part to some illnesses from which the artist may have suffered and/or to a decline in his eyesight that may have influenced both his ability to detect details and his ability to paint.

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

A hybrid approach EMD-HW for short-term forecasting of daily stock market time series data

NASA Astrophysics Data System (ADS)

Awajan, Ahmad Mohd; Ismail, Mohd Tahir

2017-08-01

Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Holt-Winter method (EMD-HW) is used to improve forecasting performances in financial time series. The strength of this EMD-HW lies in its ability to forecast non-stationary and non-linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy and offers a new forecasting method in time series. The daily stock market time series data of 11 countries is applied to show the forecasting performance of the proposed EMD-HW. Based on the three forecast accuracy measures, the results indicate that EMD-HW forecasting performance is superior to traditional Holt-Winter forecasting method.

hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction.

PubMed

Fulcher, Ben D; Jones, Nick S

2017-11-22

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Calcium homeostasis and vitamin D metabolism and expression in strongly calcifying laying birds.

PubMed

Bar, Arie

2008-12-01

Egg laying and shell calcification impose severe extra demands on ionic calcium (Ca2+) homeostasis; especially in birds characterized by their long clutches (series of eggs laid sequentially before a "pause day"). These demands induce vitamin D metabolism and expression. The metabolism of vitamin D is also altered indirectly, by other processes associated with increased demands for calcium, such as growth, bone formation and egg production. A series of intestinal, renal or bone proteins are consequently expressed in the target organs via mechanisms involving a vitamin D receptor. Some of these proteins (carbonic anhydrase, calbindin and calcium-ATPase) are also found in the uterus (eggshell gland) or are believed to be involved in calcium transport in the intestine or kidney (calcium channels). The present review deals with vitamin D metabolism and the expression of the above-mentioned proteins in birds, with special attention to the strongly calcifying laying bird.

Time series momentum and contrarian effects in the Chinese stock market

NASA Astrophysics Data System (ADS)

Shi, Huai-Long; Zhou, Wei-Xing

2017-10-01

This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-specific characteristics. Our findings indicate that there is a time series momentum effect in the short run and a contrarian effect in the long run in the Chinese stock market. The performances of the time series momentum and contrarian strategies are highly dependent on the look-back and holding periods and firm-specific characteristics.

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C ( τ ), as opposed to standard methods that decompose the time series, X ( t ), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

PubMed

Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

2014-01-01

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

Nonlinear parametric model for Granger causality of time series

NASA Astrophysics Data System (ADS)

Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

2006-06-01

The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.

Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

NASA Astrophysics Data System (ADS)

Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

2013-02-01

Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

Real-time polymerase chain reaction analysis of MDM2 and CDK4 expression using total RNA from core-needle biopsies is useful for diagnosing adipocytic tumors

PubMed Central

2014-01-01

Background Diagnosing adipocytic tumors can be challenging because it is often difficult to morphologically distinguish between benign, intermediate and malignant adipocytic tumors, and other sarcomas that are histologically similar. Recently, a number of tumor-specific chromosome translocations and associated fusion genes have been identified in adipocytic tumors and atypical lipomatous tumors/well-differentiated liposarcomas (ALT/WDL), which have a supernumerary ring and/or giant chromosome marker with amplified sequences of the MDM2 and CDK4 genes. The purpose of this study was to investigate whether quantitative real-time polymerase chain reaction (PCR) could be used to amplify MDM2 and CDK4 from total RNA samples obtained from core-needle biopsy sections for the diagnosis of ALT/WDL. Methods A series of lipoma (n = 124) and ALT/WDL (n = 44) cases were analyzed for cytogenetic analysis and lipoma fusion genes, as well as for MDM2 and CDK4 expression by real-time PCR. Moreover, the expression of MDM2 and CDK4 in whole tissue sections was compared with that in core-needle biopsy sections of the same tumor in order to determine whether real-time PCR could be used to distinguish ALT/WDL from lipoma at the preoperative stage. Results In whole tissue sections, the medians for MDM2 and CDK4 expression in ALT/WDL were higher than those in the lipomas (P < 0.05). Moreover, karyotype subdivisions with rings and/or giant chromosomes had higher MDM2 and CDK4 expression levels compared to karyotypes with 12q13-15 rearrangements, other abnormal karyotypes, and normal karyotypes (P < 0.05). On the other hand, MDM2 and CDK4 expression levels in core-needle biopsy sections were similar to those in whole-tissue sections (MDM2: P = 0.6, CDK4: P = 0.8, Wilcoxon signed-rank test). Conclusion Quantitative real-time PCR of total RNA can be used to evaluate the MDM2 and CDK4 expression levels in core-needle biopsies and may be useful for distinguishing ALT/WDL from adipocytic tumors. Thus, total RNA from core-needle biopsy sections may have potential as a routine diagnostic tool for other tumors where gene overexpression is a feature of the tumor. PMID:24965044

BIOLOGIC EFFECTS OF LOW-LEVEL IONIZING RADIATION: DISTINGUISHED LECTURER SERIES

EPA Science Inventory

This represents the first in a series of lectures sponsored by the Agency to present a range of perspectives on controversial environmental and health issues from the vantage points of distinguished scientists. The views expressed are, therefore, not necessarily the views of the ...

The First Steps Series. [Videotapes].

ERIC Educational Resources Information Center

1998

This videotape series offers emotional support and self-confidence to parents reaching out for knowledge about how young children, grow, develop, and learn. The video "Common Concerns of Parents of Young" (30 minutes) features parents expressing some universal concerns regarding their young children. Child psychologists and pediatricians offer…

An Energy-Based Similarity Measure for Time Series

NASA Astrophysics Data System (ADS)

Boudraa, Abdel-Ouahab; Cexus, Jean-Christophe; Groussat, Mathieu; Brunagel, Pierre

2007-12-01

A new similarity measure, called SimilB, for time series analysis, based on the cross-[InlineEquation not available: see fulltext.]-energy operator (2004), is introduced. [InlineEquation not available: see fulltext.] is a nonlinear measure which quantifies the interaction between two time series. Compared to Euclidean distance (ED) or the Pearson correlation coefficient (CC), SimilB includes the temporal information and relative changes of the time series using the first and second derivatives of the time series. SimilB is well suited for both nonstationary and stationary time series and particularly those presenting discontinuities. Some new properties of [InlineEquation not available: see fulltext.] are presented. Particularly, we show that [InlineEquation not available: see fulltext.] as similarity measure is robust to both scale and time shift. SimilB is illustrated with synthetic time series and an artificial dataset and compared to the CC and the ED measures.

A hybrid algorithm for clustering of time series data based on affinity search technique.

PubMed

Aghabozorgi, Saeed; Ying Wah, Teh; Herawan, Tutut; Jalab, Hamid A; Shaygan, Mohammad Amin; Jalali, Alireza

2014-01-01

Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.

A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique

PubMed Central

Aghabozorgi, Saeed; Ying Wah, Teh; Herawan, Tutut; Jalab, Hamid A.; Shaygan, Mohammad Amin; Jalali, Alireza

2014-01-01

Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets. PMID:24982966

Impressionist Landscape Cartography

NASA Astrophysics Data System (ADS)

Todd, Stella W.

2018-05-01

Cartography helps to show us the world in which we reside by providing us a framework to explore space. We can select myriad themes to represent what is relevant to our lives: physical characteristics, human behaviors, hazards, opportunities. Themes are represented on a continuum between real-world images and pure abstractions. How we define cartography and what we expect from it changes with society and technology. We are now inundated with data but we still struggle with expressing our personal geographic experiences through cartography. In this age of information we have become more cognizant of our individual experience of place and our need to determine our own paths and therefore create our own maps. In order to reflect our journey we can add individual details to cartographic products or generalize information to concentrate on what is meaningful to us. Since time and space are interrelated we experience geography by viewing the landscape as changing scenes over time. This experience is both spatial and temporal since we experience geography by moving through space. Experiencing each scene is a separate event. This paper expands the personalization of maps to include our impressions of the travel experience. Rather than add art to cartography it provides geographic reference to art. It explores the use of a series of quick sketches drawn while traveling along roads using a single drawing pad to produce a time series of interpreted landscapes. With the use of geographic time stamps from global positioning systems these sketches are converted from a drawing to a map documenting the path of movement. Although the map scale varies between sketch entries each scene impression can be linked to one or more maps of consistent scale. The result is an artistic piece that expresses a dynamic geographic experience that can be viewed in conjunction with more traditional maps. Unlike mental maps which are constructed from memory, these maps reflect our direct impressions of the landscape. The use of art can help us convey our experience.

Pooling score: an endoscopic model for evaluating severity of dysphagia

PubMed Central

Farneti, D

2008-01-01

Summary The finding of secretions and bolus pooling is of great diagnostic interest in the evaluation of subjects with swallowing disorders. Bedside evaluation alone, in subjects at risk for aspiration, can underestimate this parameter. The usefulness of endoscopic investigation for the evaluation of subjects with swallowing disorders is stressed, in order to plan treatment and follow-up. Based on endoscopic evaluation of material pooling we devised a score expressing the severity of dysphagia. This value takes into account endoscopic landmarks and other parameters of bedside evaluation. Endoscopic and bedside data were collected from a heterogeneous population of 520 consecutive patients seen in our Service over a 6-year period. By means of the test of equality of group means and logistic regression, parameters able to significantly predict aspiration in the series were identified. An ordinal number was attributed to each parameter in order to obtain scores expressing three degrees of severity of dysphagia: mild, moderate, severe. The scores can be used to guide the management of patients in a simple way, providing indications for targeted referral to the speech pathologist and for tracking the disorder over time. This investigation represents the basis for future research aimed at validating the scores in a larger case series. PMID:18646575

Ancient origin of placental expression in the growth hormone genes of anthropoid primates

PubMed Central

Papper, Zack; Jameson, Natalie M.; Romero, Roberto; Weckle, Amy L.; Mittal, Pooja; Benirschke, Kurt; Santolaya-Forgas, Joaquin; Uddin, Monica; Haig, David; Goodman, Morris; Wildman, Derek E.

2009-01-01

In anthropoid primates, growth hormone (GH) genes have undergone at least 2 independent locus expansions, one in platyrrhines (New World monkeys) and another in catarrhines (Old World monkeys and apes). In catarrhines, the GH cluster has a pituitary-expressed gene called GH1; the remaining GH genes include placental GHs and placental lactogens. Here, we provide cDNA sequence evidence that the platyrrhine GH cluster also includes at least 3 placenta expressed genes and phylogenetic evidence that placenta expressed anthropoid GH genes have undergone strong adaptive evolution, whereas pituitary-expressed GH genes have faced strict functional constraint. Our phylogenetic evidence also points to lineage-specific gene gain and loss in early placental mammalian evolution, with at least three copies of the GH gene present at the time of the last common ancestor (LCA) of primates, rodents, and laurasiatherians. Anthropoid primates and laurasiatherians share gene descendants of one of these three copies, whereas rodents and strepsirrhine primates each maintain a separate copy. Eight of the amino-acid replacements that occurred on the lineage leading to the LCA of extant anthropoids have been implicated in GH signaling at the maternal-fetal interface. Thus, placental expression of GH may have preceded the separate series of GH gene duplications that occurred in catarrhines and platyrrhines (i.e., the roles played by placenta-expressed GHs in human pregnancy may have a longer evolutionary history than previously appreciated). PMID:19805162

Ancient origin of placental expression in the growth hormone genes of anthropoid primates.

PubMed

Papper, Zack; Jameson, Natalie M; Romero, Roberto; Weckle, Amy L; Mittal, Pooja; Benirschke, Kurt; Santolaya-Forgas, Joaquin; Uddin, Monica; Haig, David; Goodman, Morris; Wildman, Derek E

2009-10-06

In anthropoid primates, growth hormone (GH) genes have undergone at least 2 independent locus expansions, one in platyrrhines (New World monkeys) and another in catarrhines (Old World monkeys and apes). In catarrhines, the GH cluster has a pituitary-expressed gene called GH1; the remaining GH genes include placental GHs and placental lactogens. Here, we provide cDNA sequence evidence that the platyrrhine GH cluster also includes at least 3 placenta expressed genes and phylogenetic evidence that placenta expressed anthropoid GH genes have undergone strong adaptive evolution, whereas pituitary-expressed GH genes have faced strict functional constraint. Our phylogenetic evidence also points to lineage-specific gene gain and loss in early placental mammalian evolution, with at least three copies of the GH gene present at the time of the last common ancestor (LCA) of primates, rodents, and laurasiatherians. Anthropoid primates and laurasiatherians share gene descendants of one of these three copies, whereas rodents and strepsirrhine primates each maintain a separate copy. Eight of the amino-acid replacements that occurred on the lineage leading to the LCA of extant anthropoids have been implicated in GH signaling at the maternal-fetal interface. Thus, placental expression of GH may have preceded the separate series of GH gene duplications that occurred in catarrhines and platyrrhines (i.e., the roles played by placenta-expressed GHs in human pregnancy may have a longer evolutionary history than previously appreciated).

Clustering Financial Time Series by Network Community Analysis

NASA Astrophysics Data System (ADS)

Piccardi, Carlo; Calatroni, Lisa; Bertoni, Fabio

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

Stable RNA markers for identification of blood and saliva stains revealed from whole genome expression analysis of time-wise degraded samples

PubMed Central

Zubakov, Dmitry; Hanekamp, Eline; Kokshoorn, Mieke; van IJcken, Wilfred

2007-01-01

Human body fluids such as blood and saliva represent the most common source of biological material found at a crime scene. Reliable tissue identification in forensic science can reveal significant insights into crime scene reconstruction and can thus contribute toward solving crimes. Limitations of existing presumptive tests for body fluid identification in forensics, which are usually based on chemoluminescence or protein analysis, are expected to be overcome by RNA-based methods, provided that stable RNA markers with tissue-specific expression patterns are available. To generate sets of stable RNA markers for reliable identification of blood and saliva stains we (1) performed whole-genome gene expression analyses on a series of time-wise degraded blood and saliva stain samples using the Affymetrix U133 plus2 GeneChip, (2) consulted expression databases to obtain additional information on tissue specificity, and (3) confirmed expression patterns of the most promising candidate genes by quantitative real-time polymerase chain reaction including additional forensically relevant tissues such as semen and vaginal secretion. Overall, we identified nine stable mRNA markers for blood and five stable mRNA markers for saliva detection showing tissue-specific expression signals in stains aged up to 180 days of age, expectedly older. Although, all of the markers were able to differentiate blood/saliva from semen samples, none of them could differentiate vaginal secretion because of the complex nature of vaginal secretion and the biological similarity of buccal and vaginal mucosa. We propose the use of these 14 stable mRNA markers for identification of blood and saliva stains in future forensic practice. Electronic supplementary material The online version of this article (doi:10.1007/s00414-007-0182-6) contains supplementary material, which is available to authorized users. PMID:17579879

Attractor States in Teaching and Learning Processes: A Study of Out-of-School Science Education

PubMed Central

Geveke, Carla H.; Steenbeek, Henderien W.; Doornenbal, Jeannette M.; Van Geert, Paul L. C.

2017-01-01

In order for out-of-school science activities that take place during school hours but outside the school context to be successful, instructors must have sufficient pedagogical content knowledge (PCK) to guarantee high-quality teaching and learning. We argue that PCK is a quality of the instructor-pupil system that is constructed in real-time interaction. When PCK is evident in real-time interaction, we define it as Expressed Pedagogical Content Knowledge (EPCK). The aim of this study is to empirically explore whether EPCK shows a systematic pattern of variation, and if so whether the pattern occurs in recurrent and temporary stable attractor states as predicted in the complex dynamic systems theory. This study concerned nine out-of-school activities in which pupils of upper primary school classes participated. A multivariate coding scheme was used to capture EPCK in real time. A principal component analysis of the time series of all the variables reduced the number of components. A cluster revealed general descriptions of the components across all cases. Cluster analyses of individual cases divided the time series into sequences, revealing High-, Low-, and Non-EPCK states. High-EPCK attractor states emerged at particular moments during activities, rather than being present all the time. Such High-EPCK attractor states were only found in a few cases, namely those where the pupils were prepared for the visit and the instructors were trained. PMID:28316578

A perturbative approach for enhancing the performance of time series forecasting.

PubMed

de Mattos Neto, Paulo S G; Ferreira, Tiago A E; Lima, Aranildo R; Vasconcelos, Germano C; Cavalcanti, George D C

2017-04-01

This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published. Copyright © 2017 Elsevier Ltd. All rights reserved.

Termite enzymes and uses thereof for in vitro conversion of lignin-containing materials to fermentable products

DOEpatents

Scharf, Michael E; Boucias, Drion G; Tartar, Aurelien; Coy, Monique R; Zhou, Xuguo; Salem, Tamer Ibrahim Zaki; Jadhao, Sanjay B; Wheeler, Marsha M

2013-05-21

The disclosure provides isolated nucleic acid molecules derived from the gut of the termite R flavipes, recombinant nucleic acid molecules comprising a vector and an isolated heterologous nucleic acid molecule operably inserted therein, whereby, when transformed into an appropriate host cell system, the heterologous nucleic acid sequence is expressed as a polypeptide having an activity similar to that when expressed in the gut of the termite R. flavipes. The recombinant nucleic acid molecules can comprise more than one heterologous nucleic acid molecule such that more than one polypeptide may be expressed by the host system. The expressed polypeptides may be substantially purified, or used in a substantially unpurified form, to be admixed with a lignocellulose source to be converted to a fermentable product such as a sugar or a mixture of sugars. One aspect of the present disclosure, therefore, encompasses methods of converting a lignified plant material to a fermentable product, the method comprising obtaining a series of isolated polypeptides of a termite, wherein the series of polypeptides cooperate to convert a plant lignocellulose to a fermentable product; and incubating the series of polypeptides with a source of lignified plant material, under conditions allowing the polypeptides to cooperatively produce a fermentable product from the lignified plant material.

A comparative assessment of R. M. Young and tipping bucket rain gauges

NASA Technical Reports Server (NTRS)

Goldhirsh, Julius; Gebo, Norman E.

1992-01-01

Rain rates as derived from standard tipping bucket rain gauges have variable integration times corresponding to the interval between bucket tips. For example, the integration time for the Weathertronics rain gauge is given by delta(T) = 15.24/R (min), where R is the rain rate expressed in mm/h and delta(T) is the time between tips expressed in minutes. It is apparent that a rain rate of 1 mm/h has an integration time in excess of 15 minutes. Rain rates larger than 15.24 mm/h will have integration times smaller than 1 minute. The integration time is dictated by the time it takes to fill a small tipping bucket where each tip gives rise to 0.254 mm of rainfall. Hence, a uniform rain rate of 1 mm/h over a 15 minute period will give rise to the same rain rate as 0 mm/h rainfall over the first 14 minutes and 15 mm/h between 14 to 15 minutes from the reference tip. Hence, the rain intensity fluctuations may not be captured with the tipping bucket rain gauge for highly variable rates encompassing lower and higher values over a given integration time. The objective of this effort is to provide an assessment of the features of the R. M. Young capacitive gauge and to compare these features with those of the standard tipping bucket rain gauge. A number of rain rate-time series derived from measurements with approximately co-located gauges are examined.

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

Multifractal analysis of the Korean agricultural market

NASA Astrophysics Data System (ADS)

Kim, Hongseok; Oh, Gabjin; Kim, Seunghwan

2011-11-01

We have studied the long-term memory effects of the Korean agricultural market using the detrended fluctuation analysis (DFA) method. In general, the return time series of various financial data, including stock indices, foreign exchange rates, and commodity prices, are uncorrelated in time, while the volatility time series are strongly correlated. However, we found that the return time series of Korean agricultural commodity prices are anti-correlated in time, while the volatility time series are correlated. The n-point correlations of time series were also examined, and it was found that a multifractal structure exists in Korean agricultural market prices.

Visibility Graph Based Time Series Analysis

PubMed Central

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it’s microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks. PMID:26571115

An immunohistochemical procedure to detect patients with paraganglioma and phaeochromocytoma with germline SDHB, SDHC, or SDHD gene mutations: a retrospective and prospective analysis

PubMed Central

van Nederveen, Francien H; Gaal, José; Favier, Judith; Korpershoek, Esther; Oldenburg, Rogier A; de Bruyn, Elly M C A; Sleddens, Hein F B M; Derkx, Pieter; Rivière, Julie; Dannenberg, Hilde; Petri, Bart-Jeroen; Komminoth, Paul; Pacak, Karel; Hop, Wim C J; Pollard, Patrick J; Mannelli, Massimo; Bayley, Jean-Pierre; Perren, Aurel; Niemann, Stephan; Verhofstad, Albert A; de Bruïne, Adriaan P; Maher, Eamonn R; Tissier, Frédérique; Méatchi, Tchao; Badoual, Cécile; Bertherat, Jérôme; Amar, Laurence; Alataki, Despoina; Van Marck, Eric; Ferrau, Francesco; François, Jerney; de Herder, Wouter W; Peeters, Mark-Paul F M Vrancken; van Linge, Anne; Lenders, Jacques W M; Gimenez-Roqueplo, Anne-Paule; de Krijger, Ronald R; Dinjens, Winand N M

2016-01-01

Summary Background Phaeochromocytomas and paragangliomas are neuro-endocrine tumours that occur sporadically and in several hereditary tumour syndromes, including the phaeochromocytoma–paraganglioma syndrome. This syndrome is caused by germline mutations in succinate dehydrogenase B (SDHB), C (SDHC), or D (SDHD) genes. Clinically, the phaeochromocytoma–paraganglioma syndrome is often unrecognised, although 10–30% of apparently sporadic phaeochromocytomas and paragangliomas harbour germline SDH-gene mutations. Despite these figures, the screening of phaeochromocytomas and paragangliomas for mutations in the SDH genes to detect phaeochromocytoma–paraganglioma syndrome is rarely done because of time and financial constraints. We investigated whether SDHB immunohistochemistry could effectively discriminate between SDH-related and non-SDH-related phaeochromocytomas and paragangliomas in large retrospective and prospective tumour series. Methods Immunohistochemistry for SDHB was done on 220 tumours. Two retrospective series of 175 phaeochromocytomas and paragangliomas with known germline mutation status for phaeochromocytoma-susceptibility or paraganglioma-susceptibility genes were investigated. Additionally, a prospective series of 45 phaeochromocytomas and paragangliomas was investigated for SDHB immunostaining followed by SDHB, SDHC, and SDHD mutation testing. Findings SDHB protein expression was absent in all 102 phaeochromocytomas and paragangliomas with an SDHB, SDHC, or SDHD mutation, but was present in all 65 paraganglionic tumours related to multiple endocrine neoplasia type 2, von Hippel–Lindau disease, and neurofibromatosis type 1. 47 (89%) of the 53 phaeochromocytomas and paragangliomas with no syndromic germline mutation showed SDHB expression. The sensitivity and specificity of the SDHB immunohistochemistry to detect the presence of an SDH mutation in the prospective series were 100% (95% CI 87–100) and 84% (60–97), respectively. Interpretation Phaeochromocytoma–paraganglioma syndrome can be diagnosed reliably by an immunohistochemical procedure. SDHB, SDHC, and SDHD germline mutation testing is indicated only in patients with SDHB-negative tumours. SDHB immunohistochemistry on phaeochromocytomas and paragangliomas could improve the diagnosis of phaeochromocytoma–paraganglioma syndrome. PMID:19576851

Quantifying memory in complex physiological time-series.

PubMed

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Quantifying Memory in Complex Physiological Time-Series

PubMed Central

Shirazi, Amir H.; Raoufy, Mohammad R.; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R.; Amodio, Piero; Jafari, G. Reza; Montagnese, Sara; Mani, Ali R.

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of “memory length” was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are ‘forgotten’ quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations. PMID:24039811

Scale-dependent intrinsic entropies of complex time series.

PubMed

Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

2016-04-13

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. © 2016 The Author(s).

A non linear analysis of human gait time series based on multifractal analysis and cross correlations

NASA Astrophysics Data System (ADS)

Muñoz-Diosdado, A.

2005-01-01

We analyzed databases with gait time series of adults and persons with Parkinson, Huntington and amyotrophic lateral sclerosis (ALS) diseases. We obtained the staircase graphs of accumulated events that can be bounded by a straight line whose slope can be used to distinguish between gait time series from healthy and ill persons. The global Hurst exponent of these series do not show tendencies, we intend that this is because some gait time series have monofractal behavior and others have multifractal behavior so they cannot be characterized with a single Hurst exponent. We calculated the multifractal spectra, obtained the spectra width and found that the spectra of the healthy young persons are almost monofractal. The spectra of ill persons are wider than the spectra of healthy persons. In opposition to the interbeat time series where the pathology implies loss of multifractality, in the gait time series the multifractal behavior emerges with the pathology. Data were collected from healthy and ill subjects as they walked in a roughly circular path and they have sensors in both feet, so we have one time series for the left foot and other for the right foot. First, we analyzed these time series separately, and then we compared both results, with direct comparison and with a cross correlation analysis. We tried to find differences in both time series that can be used as indicators of equilibrium problems.

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

PubMed

Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

2016-01-01

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

Temporal gene expression profiling of the rat knee joint capsule during immobilization-induced joint contractures.

PubMed

Wong, Kayleigh; Sun, Fangui; Trudel, Guy; Sebastiani, Paola; Laneuville, Odette

2015-05-26

Contractures of the knee joint cause disability and handicap. Recovering range of motion is recognized by arthritic patients as their preference for improved health outcome secondary only to pain management. Clinical and experimental studies provide evidence that the posterior knee capsule prevents the knee from achieving full extension. This study was undertaken to investigate the dynamic changes of the joint capsule transcriptome during the progression of knee joint contractures induced by immobilization. We performed a microarray analysis of genes expressed in the posterior knee joint capsule following induction of a flexion contracture by rigidly immobilizing the rat knee joint over a time-course of 16 weeks. Fold changes of expression values were measured and co-expressed genes were identified by clustering based on time-series analysis. Genes associated with immobilization were further analyzed to reveal pathways and biological significance and validated by immunohistochemistry on sagittal sections of knee joints. Changes in expression with a minimum of 1.5 fold changes were dominated by a decrease in expression for 7732 probe sets occurring at week 8 while the expression of 2251 probe sets increased. Clusters of genes with similar profiles of expression included a total of 162 genes displaying at least a 2 fold change compared to week 1. Functional analysis revealed ontology categories corresponding to triglyceride metabolism, extracellular matrix and muscle contraction. The altered expression of selected genes involved in the triglyceride biosynthesis pathway; AGPAT-9, and of the genes P4HB and HSP47, both involved in collagen synthesis, was confirmed by immunohistochemistry. Gene expression in the knee joint capsule was sensitive to joint immobility and provided insights into molecular mechanisms relevant to the pathophysiology of knee flexion contractures. Capsule responses to immobilization was dynamic and characterized by modulation of at least three reaction pathways; down regulation of triglyceride biosynthesis, alteration of extracellular matrix degradation and muscle contraction gene expression. The posterior knee capsule may deploy tissue-specific patterns of mRNA regulatory responses to immobilization. The identification of altered expression of genes and biochemical pathways in the joint capsule provides potential targets for the therapy of knee flexion contractures.

The examination of headache activity using time-series research designs.

PubMed

Houle, Timothy T; Remble, Thomas A; Houle, Thomas A

2005-05-01

The majority of research conducted on headache has utilized cross-sectional designs which preclude the examination of dynamic factors and principally rely on group-level effects. The present article describes the application of an individual-oriented process model using time-series analytical techniques. The blending of a time-series approach with an interactive process model allows consideration of the relationships of intra-individual dynamic processes, while not precluding the researcher to examine inter-individual differences. The authors explore the nature of time-series data and present two necessary assumptions underlying the time-series approach. The concept of shock and its contribution to headache activity is also presented. The time-series approach is not without its problems and two such problems are specifically reported: autocorrelation and the distribution of daily observations. The article concludes with the presentation of several analytical techniques suited to examine the time-series interactive process model.

Case Series Investigations in Cognitive Neuropsychology

PubMed Central

Schwartz, Myrna F.; Dell, Gary S.

2011-01-01

Case series methodology involves the systematic assessment of a sample of related patients, with the goal of understanding how and why they differ from one another. This method has become increasingly important in cognitive neuropsychology, which has long been identified with single-subject research. We review case series studies dealing with impaired semantic memory, reading, and language production, and draw attention to the affinity of this methodology for testing theories that are expressed as computational models and for addressing questions about neuroanatomy. It is concluded that case series methods usefully complement single-subject techniques. PMID:21714756

Emotion in Children's Art: Do Young Children Understand the Emotions Expressed in Other Children's Drawings?

ERIC Educational Resources Information Center

Misailidi, Plousia; Bonoti, Fotini

2008-01-01

This study examined developmental changes in children's ability to understand the emotions expressed in other children's drawings. Eighty participants, at each of four age groups--three, four, five and six years--were presented with a series of child drawings, each expressing a different emotion (happiness, sadness, anger or fear). All drawings…

Long-range correlations in time series generated by time-fractional diffusion: A numerical study

NASA Astrophysics Data System (ADS)

Barbieri, Davide; Vivoli, Alessandro

2005-09-01

Time series models showing power law tails in autocorrelation functions are common in econometrics. A special non-Markovian model for such kind of time series is provided by the random walk introduced by Gorenflo et al. as a discretization of time fractional diffusion. The time series so obtained are analyzed here from a numerical point of view in terms of autocorrelations and covariance matrices.

Exploring valid internal-control genes in Porphyra yezoensis (Bangiaceae) during stress response conditions

NASA Astrophysics Data System (ADS)

Wang, Wenlei; Wu, Xiaojie; Wang, Chao; Jia, Zhaojun; He, Linwen; Wei, Yifan; Niu, Jianfeng; Wang, Guangce

2014-07-01

To screen the stable expression genes related to the stress (strong light, dehydration and temperature shock) we applied Absolute real-time PCR technology to determine the transcription numbers of the selected test genes in P orphyra yezoensis, which has been regarded as a potential model species responding the stress conditions in the intertidal. Absolute real-time PCR technology was applied to determine the transcription numbers of the selected test genes in P orphyra yezoensis, which has been regarded as a potential model species in stress responding. According to the results of photosynthesis parameters, we observed that Y(II) and F v/ F m were significantly affected when stress was imposed on the thalli of P orphyra yezoensis, but underwent almost completely recovered under normal conditions, which were collected for the following experiments. Then three samples, which were treated with different grade stresses combined with salinity, irradiation and temperature, were collected. The transcription numbers of seven constitutive expression genes in above samples were determined after RNA extraction and cDNA synthesis. Finally, a general insight into the selection of internal control genes during stress response was obtained. We found that there were no obvious effects in terms of salinity stress (at salinity 90) on transcription of most genes used in the study. The 18S ribosomal RNA gene had the highest expression level, varying remarkably among different tested groups. RPS8 expression showed a high irregular variance between samples. GAPDH presented comparatively stable expression and could thus be selected as the internal control. EF-1α showed stable expression during the series of multiple-stress tests. Our research provided available references for the selection of internal control genes for transcripts determination of P. yezoensis.

Improving estimates of ecosystem metabolism by reducing effects of tidal advection on dissolved oxygen time series

EPA Science Inventory

In aquatic systems, time series of dissolved oxygen (DO) have been used to compute estimates of ecosystem metabolism. Central to this open-water method is the assumption that the DO time series is a Lagrangian specification of the flow field. However, most DO time series are coll...

76 FR 28897 - Magnuson-Stevens Act Provisions; Fisheries Off West Coast States; Pacific Coast Groundfish...

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... time series became closer (while depletion at the end of the time series became more divergent); (4) the agreement in the recruitment time series was much improved; (5) recruitment deviations in log space showed much closer agreement; and (6) the fishing intensity time series showed much closer...

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Federal Register 2010, 2011, 2012, 2013, 2014

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... applications. Signal-to-Noise Enhancement in Imaging Applications Using a Time-Series of Images Description of... applications that use a time-series of images. In one embodiment of the invention, a time-series of images is... Imaging Applications Using a Time-Series of Images'' (HHS Reference No. E-292- 2009/0-US-01). Related...

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... Series, any adjusted option series, and any option series until the time to expiration for such series is... existing requirement may at times discourage liquidity in particular options series because a market maker... the option is subject to the Price/Time execution algorithm, the Directed Market Maker shall receive...

A novel water quality data analysis framework based on time-series data mining.

PubMed

Deng, Weihui; Wang, Guoyin

2017-07-01

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mitragynine Attenuates Withdrawal Syndrome in Morphine-Withdrawn Zebrafish

PubMed Central

Khor, Beng-Siang; Amar Jamil, Mohd Fadzly; Adenan, Mohamad Ilham; Chong Shu-Chien, Alexander

2011-01-01

A major obstacle in treating drug addiction is the severity of opiate withdrawal syndrome, which can lead to unwanted relapse. Mitragynine is the major alkaloid compound found in leaves of Mitragyna speciosa, a plant widely used by opiate addicts to mitigate the harshness of drug withdrawal. A series of experiments was conducted to investigate the effect of mitragynine on anxiety behavior, cortisol level and expression of stress pathway related genes in zebrafish undergoing morphine withdrawal phase. Adult zebrafish were subjected to two weeks chronic morphine exposure at 1.5 mg/L, followed by withdrawal for 24 hours prior to tests. Using the novel tank diving tests, we first showed that morphine-withdrawn zebrafish display anxiety-related swimming behaviors such as decreased exploratory behavior and increased erratic movement. Morphine withdrawal also elevated whole-body cortisol levels, which confirms the phenotypic stress-like behaviors. Exposing morphine-withdrawn fish to mitragynine however attenuates majority of the stress-related swimming behaviors and concomitantly lower whole-body cortisol level. Using real-time PCR gene expression analysis, we also showed that mitragynine reduces the mRNA expression of corticotropin releasing factor receptors and prodynorphin in zebrafish brain during morphine withdrawal phase, revealing for the first time a possible link between mitragynine's ability to attenuate anxiety during opiate withdrawal with the stress-related corticotropin pathway. PMID:22205946

Geodetic imaging of tectonic deformation with InSAR

NASA Astrophysics Data System (ADS)

Fattahi, Heresh

Precise measurements of ground deformation across the plate boundaries are crucial observations to evaluate the location of strain localization and to understand the pattern of strain accumulation at depth. Such information can be used to evaluate the possible location and magnitude of future earthquakes. Interferometric Synthetic Aperture Radar (InSAR) potentially can deliver small-scale (few mm/yr) ground displacement over long distances (hundreds of kilometers) across the plate boundaries and over continents. However, Given the ground displacement as our signal of interest, the InSAR observations of ground deformation are usually affected by several sources of systematic and random noises. In this dissertation I identify several sources of systematic and random noise, develop new methods to model and mitigate the systematic noise and to evaluate the uncertainty of the ground displacement measured with InSAR. I use the developed approach to characterize the tectonic deformation and evaluate the rate of strain accumulation along the Chaman fault system, the western boundary of the India with Eurasia tectonic plates. I evaluate the bias due to the topographic residuals in the InSAR range-change time-series and develope a new method to estimate the topographic residuals and mitigate the effect from the InSAR range-change time-series (Chapter 2). I develop a new method to evaluate the uncertainty of the InSAR velocity field due to the uncertainty of the satellite orbits (Chapter 3) and a new algorithm to automatically detect and correct the phase unwrapping errors in a dense network of interferograms (Chapter 4). I develop a new approach to evaluate the impact of systematic and stochastic components of the tropospheric delay on the InSAR displacement time-series and its uncertainty (Chapter 5). Using the new InSAR time-series approach developed in the previous chapters, I study the tectonic deformation across the western boundary of the India plate with Eurasia and evaluated the rate of strain accumulation along the Chaman fault system (Chapter 5). I also evaluate the co-seismic and post-seismic displacement of a moderate M5.5 earthquake on the Ghazaband fault (Chapter 6). The developed methods to mitigate the systematic noise from InSAR time-series, significantly improve the accuracy of the InSAR displacement time-series and velocity. The approaches to evaluate the effect of the stochastic components of noise in InSAR displacement time-series enable us to obtain the variance-covariance matrix of the InSAR displacement time-series and to express their uncertainties. The effect of the topographic residuals in the InSAR range-change time-series is proportional to the perpendicular baseline history of the set of SAR acquisitions. The proposed method for topographic residual correction, efficiently corrects the displacement time-series. Evaluation of the uncertainty of velocity due to the orbital errors shows that for modern SAR satellites with precise orbits such as TerraSAR-X and Sentinel-1, the uncertainty of 0.2 mm/yr per 100 km and for older satellites with less accurate orbits such as ERS and Envisat, the uncertainty of 1.5 and 0.5mm/yr per 100 km, respectively are achievable. However, the uncertainty due to the orbital errors depends on the orbital uncertainties, the number and time span of SAR acquisitions. Contribution of the tropospheric delay to the InSAR range-change time-series can be subdivided to systematic (seasonal delay) and stochastic components. The systematic component biases the displacement times-series and velocity field as a function of the acquisition time and the non-seasonal component significantly contributes to the InSAR uncertainty. Both components are spatially correlated and therefore the covariance of noise between pixels should be considered for evaluating the uncertainty due to the random tropospheric delay. The relative velocity uncertainty due to the random tropospheric delay depends on the scatter of the random tropospheric delay, and is inversely proportional to the number of acquisitions, and the total time span covered by the SAR acquisitions. InSAR observations across the Chaman fault system shows that relative motion between India and Eurasia in the western boundary is distributed among different faults. The InSAR velocity field indicates strain localization on the Chaman fault and Ghazaband fault with slip rates of ~8 and ~16 mm/yr, respectively. High rate of strain accumulation on the Ghazaband fault and lack of evidence for rupturing the fault during the 1935 Quetta earthquake indicates that enough strain has been accumulated for large (M>7) earthquake, which threatens Balochistan and the City of Quetta. Chaman fault from latitudes ~29.5 N to ~32.5 N is creeping with a maximum surface creep rate of 8 mm/yr, which indicates that Chaman fault is only partially locked and therefore moderate earthquakes (M<7) similar to what has been recorded in last 100 years are expected.

[Spanish doctoral theses in emergency medicine (1978-2013)].

PubMed

Fernández-Guerrero, Inés María

2015-01-01

To quantitatively analyze the production of Spanish doctoral theses in emergency medicine. Quantitative synthesis of productivity indicators for 214 doctoral theses in emergency medicine found in the database (TESEO) for Spanish universities from 1978 to 2013. We processed the data in 3 ways as follows: compilation of descriptive statistics, regression analysis (correlation coefficients of determination), and modeling of linear trend (time-series analysis). Most of the thesis supervisors (84.1%) only oversaw a single project. No major supervisor of 10 or more theses was identified. Analysis of cosupervision indicated there were 1.6 supervisors per thesis. The theses were defended in 67 departments (both general and specialist departments) because no emergency medicine departments had been established. The most productive universities were 2 large ones (Universitat de Barcelona and Universidad Complutense de Madrid) and 3 medium-sized ones (Universidad de Granada, Universitat Autónoma de Barcelona, and Universidad de La Laguna). Productivity over time analyzed as the trend for 2-year periods in the time-series was expressed as a polynomial function with a correlation coefficient of determination of R2 = 0.80. Spanish doctoral research in emergency medicine has grown markedly. Work has been done in various university departments in different disciplines and specialties. The findings confirm that emergency medicine is a disciplinary field.

Real-time quantitative reverse transcription-PCR analysis of expression stability of Aggregatibacter actinomycetemcomitans fimbria-associated gene in response to photodynamic therapy.

PubMed

Pourhajibagher, Maryam; Monzavi, Abbas; Chiniforush, Nasim; Monzavi, Mohammad Moein; Sobhani, Shaghayegh; Shahabi, Sima; Bahador, Abbas

2017-06-01

Aggregatibacter actinomycetemcomitans is an etiological agent of both chronic and aggressive periodontitis. Dissemination of A. actinomycetemcomitans from the oral cavity and initiation of systemic infections has led to new approaches for treatment being needed. In this study, a series of experiments presented investigated the effect of methylene blue (MB)-mediated antimicrobial photodynamic therapy (aPDT) on cell viability and expression of fimbria-associated gene (rcpA) in A. actinomycetemcomitans. To determine the dose-depended effects of aPDT, A. actinomycetemcomitans ATCC 33384 strain photosensitized with MB was irradiated with diode laser following bacterial viability measurements. Cell-surviving assay and expression ratio of rcpA were assessed by colony forming unit and real-time quantitative reverse transcription-PCR (qRT-PCR) assays, respectively. In the current study, MB-mediated aPDT using 100μg/mL showed significant reduction in A. actinomycetemcomitans growth when compared to the control (P<0.05). Sub-lethal dose of aPDT against A. actinomycetemcomitans was 25μg/mL MB at fluency of 93.75J/cm 2 . Sub-lethal dose of aPDT could lead to about four-fold suppression of expression of rcpA. High doses of MB-mediated aPDT could potentially exhibit antimicrobial activity, and the expression of rcpA as an important virulence factor of this strain is reduced in cells surviving aPDT with MB. So, aPDT can be a valuable tool for the treatment of A. actinomycetemcomitans infections. Copyright © 2017 Elsevier B.V. All rights reserved.

Circadian regulation of reproduction: from gamete to offspring.

PubMed

Boden, M J; Varcoe, T J; Kennaway, D J

2013-12-01

Few challenges are more critical to the survival of a species than reproduction. To ensure reproductive success, myriad aspects of physiology and behaviour need to be tightly orchestrated within the animal, as well as timed appropriately with the external environment. This is accomplished through an endogenous circadian timing system generated at the cellular level through a series of interlocked transcription/translation feedback loops, leading to the overt expression of circadian rhythms. These expression patterns are found throughout the body, and are intimately interwoven with both the timing and function of the reproductive process. In this review we highlight the many aspects of reproductive physiology in which circadian rhythms are known to play a role, including regulation of the estrus cycle, the LH surge and ovulation, the production and maturation of sperm and the timing of insemination and fertilisation. We will also describe roles for circadian rhythms in support of the preimplantation embryo in the oviduct, implantation/placentation, as well as the control of parturition and early postnatal life. There are several key differences in physiology between humans and the model systems used for the study of circadian disruption, and these challenges to interpretation will be discussed as part of this review. Copyright © 2013 Elsevier Ltd. All rights reserved.

Assessing the biophysical and socio-economic potential of Sustainable Land Management and Water Harvesting Technologies for rainfed agriculture across semi-arid Africa.

NASA Astrophysics Data System (ADS)

Irvine, Brian; Fleskens, Luuk; Kirkby, Mike

2016-04-01

Stakeholders in recent EU projects identified soil erosion as the most frequent driver of land degradation in semi-arid environments. In a number of sites, historic land management and rainfall variability are recognised as contributing to the serious environmental impact. In order to consider the potential of sustainable land management and water harvesting techniques stakeholders and study sites from the projects selected and trialled both local technologies and promising technologies reported from other sites . The combined PESERA and DESMICE modelling approach considered the regional effects of the technologies in combating desertification both in environmental and socio-economical terms. Initial analysis was based on long term average climate data with the model run to equilibrium. Current analysis, primarily based on the WAHARA study sites considers rainfall variability more explicitly in time series mode. The PESERA-DESMICE approach considers the difference between a baseline scenario and a (water harvesting) technology scenario, typically, in terms of productivity, financial viability and scope for reducing erosion risk. A series of 50 year rainfall realisations are generated from observed data to capture a full range of the climatic variability. Each realisation provides a unique time-series of rainfall and through modelling can provide a simulated time-series of crop yield and erosion risk for both baseline conditions and technology scenarios. Subsequent realisations and model simulations add to an envelope of the potential crop yield and cost-benefit relations. The development of such envelopes helps express the agricultural and erosional risk associated with climate variability and the potential for conservation measures to absorb the risk, highlighting the probability of achieving a given crop yield or erosion limit. Information that can directly inform or influence the local adoption of conservation measures under the climatic variability in semi-arid areas

Nonequilibrium Green's function theory for nonadiabatic effects in quantum electron transport

NASA Astrophysics Data System (ADS)

Kershaw, Vincent F.; Kosov, Daniel S.

2017-12-01

We develop nonequilibrium Green's function-based transport theory, which includes effects of nonadiabatic nuclear motion in the calculation of the electric current in molecular junctions. Our approach is based on the separation of slow and fast time scales in the equations of motion for Green's functions by means of the Wigner representation. Time derivatives with respect to central time serve as a small parameter in the perturbative expansion enabling the computation of nonadiabatic corrections to molecular Green's functions. Consequently, we produce a series of analytic expressions for non-adiabatic electronic Green's functions (up to the second order in the central time derivatives), which depend not solely on the instantaneous molecular geometry but likewise on nuclear velocities and accelerations. An extended formula for electric current is derived which accounts for the non-adiabatic corrections. This theory is concisely illustrated by the calculations on a model molecular junction.

Nonequilibrium Green's function theory for nonadiabatic effects in quantum electron transport.

PubMed

Kershaw, Vincent F; Kosov, Daniel S

2017-12-14

We develop nonequilibrium Green's function-based transport theory, which includes effects of nonadiabatic nuclear motion in the calculation of the electric current in molecular junctions. Our approach is based on the separation of slow and fast time scales in the equations of motion for Green's functions by means of the Wigner representation. Time derivatives with respect to central time serve as a small parameter in the perturbative expansion enabling the computation of nonadiabatic corrections to molecular Green's functions. Consequently, we produce a series of analytic expressions for non-adiabatic electronic Green's functions (up to the second order in the central time derivatives), which depend not solely on the instantaneous molecular geometry but likewise on nuclear velocities and accelerations. An extended formula for electric current is derived which accounts for the non-adiabatic corrections. This theory is concisely illustrated by the calculations on a model molecular junction.

Acoustic emission monitoring and critical failure identification of bridge cable damage

NASA Astrophysics Data System (ADS)

Li, Dongsheng; Ou, Jinping

2008-03-01

Acoustic emission (AE) characteristic parameters of bridge cable damage were obtained on tensile test. The testing results show that the AE parameter analysis method based on correlation figure of count, energy, duration time, amplitude and time can express the whole damage course, and can correctly judge the signal difference of broken wire and unbroken wire. It found the bridge cable AE characteristics aren't apparent before yield deformation, however they are increasing after yield deformation, at the time of breaking, and they reach to maximum. At last, the bridge cable damage evolution law is studied applying the AE characteristic parameter time series fractal theory. In the initial and middle stage of loading, the AE fractal value of bridge cable is unsteady. The fractal value reaches to the minimum at the critical point of failure. According to this changing law, it is approached how to make dynamic assessment and estimation of damage degrees.

Stochastic sediment property inversion in Shallow Water 06.

PubMed

Michalopoulou, Zoi-Heleni

2017-11-01

Received time-series at a short distance from the source allow the identification of distinct paths; four of these are direct, surface and bottom reflections, and sediment reflection. In this work, a Gibbs sampling method is used for the estimation of the arrival times of these paths and the corresponding probability density functions. The arrival times for the first three paths are then employed along with linearization for the estimation of source range and depth, water column depth, and sound speed in the water. Propagating densities of arrival times through the linearized inverse problem, densities are also obtained for the above parameters, providing maximum a posteriori estimates. These estimates are employed to calculate densities and point estimates of sediment sound speed and thickness using a non-linear, grid-based model. Density computation is an important aspect of this work, because those densities express the uncertainty in the inversion for sediment properties.

Expression of HIWI in human esophageal squamous cell carcinoma is significantly associated with poorer prognosis

PubMed Central

2009-01-01

Background HIWI, the human homologue of Piwi family, is present in CD34+ hematopoietic stem cells and germ cells, but not in well-differentiated cell populations, indicating that HIWI may play an impotent role in determining or maintaining stemness of these cells. That HIWI expression has been detected in several type tumours may suggest its association with clinical outcome in cancer patients. Methods With the methods of real-time PCR, western blot, immunocytochemistry and immunohistochemistry, the expression of HIWI in three esophageal squamous cancer cell lines KYSE70, KYSE140 and KYSE450 has been characterized. Then, we investigated HIWI expression in a series of 153 esophageal squamous cell carcinomas using immunohistochemistry and explored its association with clinicopathological features. Results The expression of HIWI was observed in tumour cell nuclei or/and cytoplasm in 137 (89.5%) cases, 16 (10.5%) cases were negative in both nuclei and cytoplasm. 86 (56.2%) were strongly positive in cytoplasm, while 49 (32.0%) were strongly positive in nuclei. The expression level of HIWI in cytoplasm of esophageal cancer cells was significantly associated with histological grade (P = 0.011), T stage (P = 0.035), and clinic outcome (P < 0.001), while there was no correlation between the nuclear HIWI expression and clinicopathological features. Conclusion The expression of HIWI in the cytoplasm of esophageal cancer cells is significantly associated with higher histological grade, clinical stage and poorer clinical outcome, indicating its possible involvement in cancer development. PMID:19995427

Climate Prediction Center - Stratosphere: Polar Stratosphere and Ozone

Science.gov Websites

depletion processes can occur. In addition, the latitudinal-time cross sections shows the thermal evolution UV Daily Dosage Estimate South Polar Vertical Ozone Profile Time Series of Size of S.H. Polar Vortex Time Series of Size of S.H. PSC Temperature Time Series of Size of N.H. Polar Vortex Time Series of

Using forbidden ordinal patterns to detect determinism in irregularly sampled time series.

PubMed

Kulp, C W; Chobot, J M; Niskala, B J; Needhammer, C J

2016-02-01

It is known that when symbolizing a time series into ordinal patterns using the Bandt-Pompe (BP) methodology, there will be ordinal patterns called forbidden patterns that do not occur in a deterministic series. The existence of forbidden patterns can be used to identify deterministic dynamics. In this paper, the ability to use forbidden patterns to detect determinism in irregularly sampled time series is tested on data generated from a continuous model system. The study is done in three parts. First, the effects of sampling time on the number of forbidden patterns are studied on regularly sampled time series. The next two parts focus on two types of irregular-sampling, missing data and timing jitter. It is shown that forbidden patterns can be used to detect determinism in irregularly sampled time series for low degrees of sampling irregularity (as defined in the paper). In addition, comments are made about the appropriateness of using the BP methodology to symbolize irregularly sampled time series.

Sensor-Generated Time Series Events: A Definition Language

PubMed Central

Anguera, Aurea; Lara, Juan A.; Lizcano, David; Martínez, Maria Aurora; Pazos, Juan

2012-01-01

There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest, known as events, whereas the remainder of the time series contains hardly any useful information. In these domains, there is a need for mechanisms to identify and locate such events. In this paper, we propose an events definition language that is general enough to be used to easily and naturally define events in time series recorded by sensors in any domain. The proposed language has been applied to the definition of time series events generated within the branch of medicine dealing with balance-related functions in human beings. A device, called posturograph, is used to study balance-related functions. The platform has four sensors that record the pressure intensity being exerted on the platform, generating four interrelated time series. As opposed to the existing ad hoc proposals, the results confirm that the proposed language is valid, that is generally applicable and accurate, for identifying the events contained in the time series.

Tom Didn't Say Anything.

ERIC Educational Resources Information Center

Fawcett, Gay

1994-01-01

Accused of promoting everything from Satanism to homosexuality, the "Impressions" reading series has come under attack in many of the 34 states using it. A teacher educator expresses chagrin at her "dream" students' half-hearted reactions to controversial articles about a series that they had judged superior. The silence of one…

GPN Film Catalog 1972.

ERIC Educational Resources Information Center

Nebraska Univ., Lincoln. Great Plains National Instructional Television Library.

The films described in this catalog were produced by schools or school-related organizations and were designed to meet the "relevant needs expressed by a broad spectrum of media personnel, students, and educators across the country." The catalog describes seventeen series and eight single films. For each of the series a description is presented…

Homogenising time series: Beliefs, dogmas and facts

NASA Astrophysics Data System (ADS)

Domonkos, P.

2010-09-01

For obtaining reliable information about climate change and climate variability the use of high quality data series is essentially important, and one basic tool of quality improvements is the statistical homogenisation of observed time series. In the recent decades large number of homogenisation methods has been developed, but the real effects of their application on time series are still not known entirely. The ongoing COST HOME project (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. The author believes that several old theoretical rules have to be re-evaluated. Some examples of the hot questions, a) Statistically detected change-points can be accepted only with the confirmation of metadata information? b) Do semi-hierarchic algorithms for detecting multiple change-points in time series function effectively in practise? c) Is it good to limit the spatial comparison of candidate series with up to five other series in the neighbourhood? Empirical results - those from the COST benchmark, and other experiments too - show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities seem like part of the climatic variability, thus the pure application of classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality, than in raw time series. The developers and users of homogenisation methods have to bear in mind that the eventual purpose of homogenisation is not to find change-points, but to have the observed time series with statistical properties those characterise well the climate change and climate variability.

Contraction Options and Optimal Multiple-Stopping in Spectrally Negative Lévy Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yamazaki, Kazutoshi, E-mail: kyamazak@kansai-u.ac.jp

This paper studies the optimal multiple-stopping problem arising in the context of the timing option to withdraw from a project in stages. The profits are driven by a general spectrally negative Lévy process. This allows the model to incorporate sudden declines of the project values, generalizing greatly the classical geometric Brownian motion model. We solve the one-stage case as well as the extension to the multiple-stage case. The optimal stopping times are of threshold-type and the value function admits an expression in terms of the scale function. A series of numerical experiments are conducted to verify the optimality and tomore » evaluate the efficiency of the algorithm.« less

Data assimilation using a GPU accelerated path integral Monte Carlo approach

NASA Astrophysics Data System (ADS)

Quinn, John C.; Abarbanel, Henry D. I.

2011-09-01

The answers to data assimilation questions can be expressed as path integrals over all possible state and parameter histories. We show how these path integrals can be evaluated numerically using a Markov Chain Monte Carlo method designed to run in parallel on a graphics processing unit (GPU). We demonstrate the application of the method to an example with a transmembrane voltage time series of a simulated neuron as an input, and using a Hodgkin-Huxley neuron model. By taking advantage of GPU computing, we gain a parallel speedup factor of up to about 300, compared to an equivalent serial computation on a CPU, with performance increasing as the length of the observation time used for data assimilation increases.

A Novel Strategy for Selection and Validation of Reference Genes in Dynamic Multidimensional Experimental Design in Yeast

PubMed Central

Cankorur-Cetinkaya, Ayca; Dereli, Elif; Eraslan, Serpil; Karabekmez, Erkan; Dikicioglu, Duygu; Kirdar, Betul

2012-01-01

Background Understanding the dynamic mechanism behind the transcriptional organization of genes in response to varying environmental conditions requires time-dependent data. The dynamic transcriptional response obtained by real-time RT-qPCR experiments could only be correctly interpreted if suitable reference genes are used in the analysis. The lack of available studies on the identification of candidate reference genes in dynamic gene expression studies necessitates the identification and the verification of a suitable gene set for the analysis of transient gene expression response. Principal Findings In this study, a candidate reference gene set for RT-qPCR analysis of dynamic transcriptional changes in Saccharomyces cerevisiae was determined using 31 different publicly available time series transcriptome datasets. Ten of the twelve candidates (TPI1, FBA1, CCW12, CDC19, ADH1, PGK1, GCN4, PDC1, RPS26A and ARF1) we identified were not previously reported as potential reference genes. Our method also identified the commonly used reference genes ACT1 and TDH3. The most stable reference genes from this pool were determined as TPI1, FBA1, CDC19 and ACT1 in response to a perturbation in the amount of available glucose and as FBA1, TDH3, CCW12 and ACT1 in response to a perturbation in the amount of available ammonium. The use of these newly proposed gene sets outperformed the use of common reference genes in the determination of dynamic transcriptional response of the target genes, HAP4 and MEP2, in response to relaxation from glucose and ammonium limitations, respectively. Conclusions A candidate reference gene set to be used in dynamic real-time RT-qPCR expression profiling in yeast was proposed for the first time in the present study. Suitable pools of stable reference genes to be used under different experimental conditions could be selected from this candidate set in order to successfully determine the expression profiles for the genes of interest. PMID:22675547

Causal Inference and the Comparative Interrupted Time Series Design: Findings from Within-Study Comparisons

ERIC Educational Resources Information Center

St. Clair, Travis; Hallberg, Kelly; Cook, Thomas D.

2014-01-01

Researchers are increasingly using comparative interrupted time series (CITS) designs to estimate the effects of programs and policies when randomized controlled trials are not feasible. In a simple interrupted time series design, researchers compare the pre-treatment values of a treatment group time series to post-treatment values in order to…

Time Series Model Identification and Prediction Variance Horizon.

DTIC Science & Technology

1980-06-01

stationary time series Y(t). -6- In terms of p(v), the definition of the three time series memory types is: No Memory Short Memory Long Memory X IP (v)I 0 0...X lp(v)l < - I IP (v) = v=1 v=l v=l Within short memory time series there are three types whose classification in terms of correlation functions is...1974) "Some Recent Advances in Time Series Modeling", TEEE Transactions on Automatic ControZ, VoZ . AC-19, No. 6, December, 723-730. Parzen, E. (1976) "An

Extending nonlinear analysis to short ecological time series.

PubMed

Hsieh, Chih-hao; Anderson, Christian; Sugihara, George

2008-01-01

Nonlinearity is important and ubiquitous in ecology. Though detectable in principle, nonlinear behavior is often difficult to characterize, analyze, and incorporate mechanistically into models of ecosystem function. One obvious reason is that quantitative nonlinear analysis tools are data intensive (require long time series), and time series in ecology are generally short. Here we demonstrate a useful method that circumvents data limitation and reduces sampling error by combining ecologically similar multispecies time series into one long time series. With this technique, individual ecological time series containing as few as 20 data points can be mined for such important information as (1) significantly improved forecast ability, (2) the presence and location of nonlinearity, and (3) the effective dimensionality (the number of relevant variables) of an ecological system.

Sythesis of MCMC and Belief Propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahn, Sungsoo; Chertkov, Michael; Shin, Jinwoo

Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most popular algorithms for computational inference in Graphical Models (GM). In principle, MCMC is an exact probabilistic method which, however, often suffers from exponentially slow mixing. In contrast, BP is a deterministic method, which is typically fast, empirically very successful, however in general lacking control of accuracy over loopy graphs. In this paper, we introduce MCMC algorithms correcting the approximation error of BP, i.e., we provide a way to compensate for BP errors via a consecutive BP-aware MCMC. Our framework is based on the Loop Calculus (LC) approach whichmore » allows to express the BP error as a sum of weighted generalized loops. Although the full series is computationally intractable, it is known that a truncated series, summing up all 2-regular loops, is computable in polynomial-time for planar pair-wise binary GMs and it also provides a highly accurate approximation empirically. Motivated by this, we first propose a polynomial-time approximation MCMC scheme for the truncated series of general (non-planar) pair-wise binary models. Our main idea here is to use the Worm algorithm, known to provide fast mixing in other (related) problems, and then design an appropriate rejection scheme to sample 2-regular loops. Furthermore, we also design an efficient rejection-free MCMC scheme for approximating the full series. The main novelty underlying our design is in utilizing the concept of cycle basis, which provides an efficient decomposition of the generalized loops. In essence, the proposed MCMC schemes run on transformed GM built upon the non-trivial BP solution, and our experiments show that this synthesis of BP and MCMC outperforms both direct MCMC and bare BP schemes.« less

Conventional and advanced time series estimation: application to the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database, 1993-2006.

PubMed

Moran, John L; Solomon, Patricia J

2011-02-01

Time series analysis has seen limited application in the biomedical Literature. The utility of conventional and advanced time series estimators was explored for intensive care unit (ICU) outcome series. Monthly mean time series, 1993-2006, for hospital mortality, severity-of-illness score (APACHE III), ventilation fraction and patient type (medical and surgical), were generated from the Australia and New Zealand Intensive Care Society adult patient database. Analyses encompassed geographical seasonal mortality patterns, series structural time changes, mortality series volatility using autoregressive moving average and Generalized Autoregressive Conditional Heteroscedasticity models in which predicted variances are updated adaptively, and bivariate and multivariate (vector error correction models) cointegrating relationships between series. The mortality series exhibited marked seasonality, declining mortality trend and substantial autocorrelation beyond 24 lags. Mortality increased in winter months (July-August); the medical series featured annual cycling, whereas the surgical demonstrated long and short (3-4 months) cycling. Series structural breaks were apparent in January 1995 and December 2002. The covariance stationary first-differenced mortality series was consistent with a seasonal autoregressive moving average process; the observed conditional-variance volatility (1993-1995) and residual Autoregressive Conditional Heteroscedasticity effects entailed a Generalized Autoregressive Conditional Heteroscedasticity model, preferred by information criterion and mean model forecast performance. Bivariate cointegration, indicating long-term equilibrium relationships, was established between mortality and severity-of-illness scores at the database level and for categories of ICUs. Multivariate cointegration was demonstrated for {log APACHE III score, log ICU length of stay, ICU mortality and ventilation fraction}. A system approach to understanding series time-dependence may be established using conventional and advanced econometric time series estimators. © 2010 Blackwell Publishing Ltd.

Sea change: Charting the course for biogeochemical ocean time-series research in a new millennium

NASA Astrophysics Data System (ADS)

Church, Matthew J.; Lomas, Michael W.; Muller-Karger, Frank

2013-09-01

Ocean time-series provide vital information needed for assessing ecosystem change. This paper summarizes the historical context, major program objectives, and future research priorities for three contemporary ocean time-series programs: The Hawaii Ocean Time-series (HOT), the Bermuda Atlantic Time-series Study (BATS), and the CARIACO Ocean Time-Series. These three programs operate in physically and biogeochemically distinct regions of the world's oceans, with HOT and BATS located in the open-ocean waters of the subtropical North Pacific and North Atlantic, respectively, and CARIACO situated in the anoxic Cariaco Basin of the tropical Atlantic. All three programs sustain near-monthly shipboard occupations of their field sampling sites, with HOT and BATS beginning in 1988, and CARIACO initiated in 1996. The resulting data provide some of the only multi-disciplinary, decadal-scale determinations of time-varying ecosystem change in the global ocean. Facilitated by a scoping workshop (September 2010) sponsored by the Ocean Carbon Biogeochemistry (OCB) program, leaders of these time-series programs sought community input on existing program strengths and for future research directions. Themes that emerged from these discussions included: 1. Shipboard time-series programs are key to informing our understanding of the connectivity between changes in ocean-climate and biogeochemistry 2. The scientific and logistical support provided by shipboard time-series programs forms the backbone for numerous research and education programs. Future studies should be encouraged that seek mechanistic understanding of ecological interactions underlying the biogeochemical dynamics at these sites. 3. Detecting time-varying trends in ocean properties and processes requires consistent, high-quality measurements. Time-series must carefully document analytical procedures and, where possible, trace the accuracy of analyses to certified standards and internal reference materials. 4. Leveraged implementation, testing, and validation of autonomous and remote observing technologies at time-series sites provide new insights into spatiotemporal variability underlying ecosystem changes. 5. The value of existing time-series data for formulating and validating ecosystem models should be promoted. In summary, the scientific underpinnings of ocean time-series programs remain as strong and important today as when these programs were initiated. The emerging data inform our knowledge of the ocean's biogeochemistry and ecology, and improve our predictive capacity about planetary change.

Time course expression of Foxo transcription factors in skeletal muscle following corticosteroid administration

PubMed Central

Cho, John E.; Fournier, Mario; Da, Xiaoyu

2010-01-01

Increased expression of forkhead box O (Foxo) transcription factors were reported in cultured myotubes and mouse limb muscle with corticosteroid (CS) treatment. We previously reported that administration of CS to rats resulted in muscle fiber atrophy only by day 7. The aim of this study, therefore, was to evaluate the time-course changes in the expression of Foxo transcription factors and muscle-specific ubiquitin E3 ligases in rat limb muscle following CS administration. Triamcinolone (TRI; 1 mg · kg−1 · day−1 im) was administered for 1, 3, or 7 days. Control (CTL) rats were given saline. Muscle mRNA was analyzed by real-time RT-PCR. Compared with CTL, body weights of TRI-treated animals decreased by 3, 12, and 21% at days 1, 3, and 7, respectively. Muscle IGF-1 mRNA levels decreased by 33, 65, and 58% at days 1, 3, and 7 in TRI-treated rats compared with CTL. Levels of phosphorylated Akt were 28, 50, and 36% lower in TRI animals at these time points. Foxo1 mRNA increased progressively by 1.2-, 1.4-, and 2.5-fold at days 1, 3, and 7 in TRI animals. Similar changes were noted in the expression of Foxo3a mRNA (1.3-, 1.4-, and 2.6-fold increments). By contrast, Foxo4 mRNA was not significantly changed in TRI animals. With TRI, muscle atrophy F box/Atrogin-1 increased by 1.8-, 4.1-, and 7.5-fold at days 1, 3, and 7 compared with CTL rats. By contrast, muscle RING finger 1 increased only from day 7 (2.7-fold). Gradual reduction in IGF-I expression with TRI over the time series paralleled that of Akt. These findings are consistent with a progressive stimulus to muscle protein degradation and the need to process/remove disassembled muscle proteins via the ubiquitin-proteasome system. Elucidating the dynamic catabolic responses to CS challenge is important in understanding the mechanisms underlying muscle atrophy and therapeutic measures to offset this. PMID:19850732

Characterization of time series via Rényi complexity-entropy curves

NASA Astrophysics Data System (ADS)

Jauregui, M.; Zunino, L.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

2018-05-01

One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.

Long-term memory and volatility clustering in high-frequency price changes

NASA Astrophysics Data System (ADS)

oh, Gabjin; Kim, Seunghwan; Eom, Cheoljun

2008-02-01

We studied the long-term memory in diverse stock market indices and foreign exchange rates using Detrended Fluctuation Analysis (DFA). For all high-frequency market data studied, no significant long-term memory property was detected in the return series, while a strong long-term memory property was found in the volatility time series. The possible causes of the long-term memory property were investigated using the return data filtered by the AR(1) model, reflecting the short-term memory property, the GARCH(1,1) model, reflecting the volatility clustering property, and the FIGARCH model, reflecting the long-term memory property of the volatility time series. The memory effect in the AR(1) filtered return and volatility time series remained unchanged, while the long-term memory property diminished significantly in the volatility series of the GARCH(1,1) filtered data. Notably, there is no long-term memory property, when we eliminate the long-term memory property of volatility by the FIGARCH model. For all data used, although the Hurst exponents of the volatility time series changed considerably over time, those of the time series with the volatility clustering effect removed diminish significantly. Our results imply that the long-term memory property of the volatility time series can be attributed to the volatility clustering observed in the financial time series.

Modeling seasonal variation of hip fracture in Montreal, Canada.

PubMed

Modarres, Reza; Ouarda, Taha B M J; Vanasse, Alain; Orzanco, Maria Gabriela; Gosselin, Pierre

2012-04-01

The investigation of the association of the climate variables with hip fracture incidences is important in social health issues. This study examined and modeled the seasonal variation of monthly population based hip fracture rate (HFr) time series. The seasonal ARIMA time series modeling approach is used to model monthly HFr incidences time series of female and male patients of the ages 40-74 and 75+ of Montreal, Québec province, Canada, in the period of 1993-2004. The correlation coefficients between meteorological variables such as temperature, snow depth, rainfall depth and day length and HFr are significant. The nonparametric Mann-Kendall test for trend assessment and the nonparametric Levene's test and Wilcoxon's test for checking the difference of HFr before and after change point are also used. The seasonality in HFr indicated sharp difference between winter and summer time. The trend assessment showed decreasing trends in HFr of female and male groups. The nonparametric test also indicated a significant change of the mean HFr. A seasonal ARIMA model was applied for HFr time series without trend and a time trend ARIMA model (TT-ARIMA) was developed and fitted to HFr time series with a significant trend. The multi criteria evaluation showed the adequacy of SARIMA and TT-ARIMA models for modeling seasonal hip fracture time series with and without significant trend. In the time series analysis of HFr of the Montreal region, the effects of the seasonal variation of climate variables on hip fracture are clear. The Seasonal ARIMA model is useful for modeling HFr time series without trend. However, for time series with significant trend, the TT-ARIMA model should be applied for modeling HFr time series. Copyright © 2011 Elsevier Inc. All rights reserved.

Smoothing of climate time series revisited

NASA Astrophysics Data System (ADS)

Mann, Michael E.

2008-08-01

We present an easily implemented method for smoothing climate time series, generalizing upon an approach previously described by Mann (2004). The method adaptively weights the three lowest order time series boundary constraints to optimize the fit with the raw time series. We apply the method to the instrumental global mean temperature series from 1850-2007 and to various surrogate global mean temperature series from 1850-2100 derived from the CMIP3 multimodel intercomparison project. These applications demonstrate that the adaptive method systematically out-performs certain widely used default smoothing methods, and is more likely to yield accurate assessments of long-term warming trends.

Click Chemistry-based Discovery of [3-Hydroxy-5-(1H-1,2,3-triazol-4-yl)picolinoyl]glycines as Orally Active Hypoxia Inducing Factor Prolyl Hydroxylase Inhibitors with Favorable Safety Profiles for the Treatment of Anemia.

PubMed

Wu, Yue; Jiang, Zhensheng; Li, Zhihong; Gu, Jing; You, Qi-Dong; Zhang, Xiaojin

2018-06-01

As a gene associated with anemia, the erythropoiesis gene is physiologically expressed under hypoxia regulated by hypoxia-inducing factor-α (HIF-α). Thus, stabilizing HIF-α is a potent strategy to stimulate the expression and secretion of erythropoiesis. In this study we applied click chemistry to the discovery of HIF prolyl hydroxylase 2 (HIF-PHD2) inhibitors for the first time and a series of triazole compounds showed preferable inhibitory activity in fluorescence polarization assay. Of particular note was the orally active HIF-PHD inhibitor 15i (IC50 = 62.23 nM), which was almost ten times more active than the phase III drug FG-4592 (IC50 = 591.4 nM). Furthermore, it can upregulate the hemoglobin of cisplatin induced anemia mice (120 g/L) to normal levels (160 g/L) with no apparent toxicity observed in vivo. These results confirm that triazole compound 15i is a promising candidate for the treatment of renal anemia.

Accurate evaluation for the biofilm-activated sludge reactor using graphical techniques

NASA Astrophysics Data System (ADS)

Fouad, Moharram; Bhargava, Renu

2018-05-01

A complete graphical solution is obtained for the completely mixed biofilm-activated sludge reactor (hybrid reactor). The solution consists of a series of curves deduced from the principal equations of the hybrid system after converting them in dimensionless form. The curves estimate the basic parameters of the hybrid system such as suspended biomass concentration, sludge residence time, wasted mass of sludge, and food to biomass ratio. All of these parameters can be expressed as functions of hydraulic retention time, influent substrate concentration, substrate concentration in the bulk, stagnant liquid layer thickness, and the minimum substrate concentration which can maintain the biofilm growth in addition to the basic kinetics of the activated sludge process in which all these variables are expressed in a dimensionless form. Compared to other solutions of such system these curves are simple, easy to use, and provide an accurate tool for analyzing such system based on fundamental principles. Further, these curves may be used as a quick tool to get the effect of variables change on the other parameters and the whole system.

Temperature aspect of degradation of electrochemical double-layer capacitors (EDLC)

NASA Astrophysics Data System (ADS)

Baek, Dong-Cheon; Kim, Hyun-Ho; Lee, Soon-Bok

2015-03-01

Electric double layer capacitors (EDLC) cells have a process variation and temperature dependency in capacitance so that balancing is required when they are connected in series, which includes electronic voltage management based on capacitance monitoring. This paper measured temperature aspect of capacitance periodically to monitor health and degradation behavior of EDLC stressed under high temperatures and zero below temperatures respectively, which enables estimation of the state of health (SOH) regardless of temperature. At high temperature, capacitance saturation and delayed expression of degradation was observed. After cyclic stress at zero below temperature, less effective degradation and time recovery phenomenon were occurred.

Forecasting and analyzing high O3 time series in educational area through an improved chaotic approach

NASA Astrophysics Data System (ADS)

Hamid, Nor Zila Abd; Adenan, Nur Hamiza; Noorani, Mohd Salmi Md

2017-08-01

Forecasting and analyzing the ozone (O3) concentration time series is important because the pollutant is harmful to health. This study is a pilot study for forecasting and analyzing the O3 time series in one of Malaysian educational area namely Shah Alam using chaotic approach. Through this approach, the observed hourly scalar time series is reconstructed into a multi-dimensional phase space, which is then used to forecast the future time series through the local linear approximation method. The main purpose is to forecast the high O3 concentrations. The original method performed poorly but the improved method addressed the weakness thereby enabling the high concentrations to be successfully forecast. The correlation coefficient between the observed and forecasted time series through the improved method is 0.9159 and both the mean absolute error and root mean squared error are low. Thus, the improved method is advantageous. The time series analysis by means of the phase space plot and Cao method identified the presence of low-dimensional chaotic dynamics in the observed O3 time series. Results showed that at least seven factors affect the studied O3 time series, which is consistent with the listed factors from the diurnal variations investigation and the sensitivity analysis from past studies. In conclusion, chaotic approach has been successfully forecast and analyzes the O3 time series in educational area of Shah Alam. These findings are expected to help stakeholders such as Ministry of Education and Department of Environment in having a better air pollution management.

Transformation-cost time-series method for analyzing irregularly sampled data

NASA Astrophysics Data System (ADS)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G. Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations—with associated costs—to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

Transformation-cost time-series method for analyzing irregularly sampled data.

PubMed

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

a Method of Time-Series Change Detection Using Full Polsar Images from Different Sensors

NASA Astrophysics Data System (ADS)

Liu, W.; Yang, J.; Zhao, J.; Shi, H.; Yang, L.

2018-04-01

Most of the existing change detection methods using full polarimetric synthetic aperture radar (PolSAR) are limited to detecting change between two points in time. In this paper, a novel method was proposed to detect the change based on time-series data from different sensors. Firstly, the overall difference image of a time-series PolSAR was calculated by ominous statistic test. Secondly, difference images between any two images in different times ware acquired by Rj statistic test. Generalized Gaussian mixture model (GGMM) was used to obtain time-series change detection maps in the last step for the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection by using the time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can detect the time-series change from different sensors.

Expression of beta-expansins is correlated with internodal elongation in deepwater rice.

PubMed

Lee, Y; Kende, H

2001-10-01

Fourteen putative rice (Oryza sativa) beta-expansin genes, Os-EXPB1 through Os-EXPB14, were identified in the expressed sequence tag and genomic databases. The DNA and deduced amino acid sequences are highly conserved in all 14 beta-expansins. They have a series of conserved C (cysteine) residues in the N-terminal half of the protein, an HFD (histidine-phenylalanine-aspartate) motif in the central region, and a series of W (tryptophan) residues near the carboxyl terminus. Five beta-expansin genes are expressed in deepwater rice internodes, with especially high transcript levels in the growing region. Expression of four beta-expansin genes in the internode was induced by treatment with gibberellin and by wounding. The wound response resulted from excising stem sections or from piercing pinholes into the stem of intact plants. The level of wound-induced beta-expansin transcripts declined rapidly 5 h after cutting of stem sections. We conclude that the expression of beta-expansin genes is correlated with rapid elongation of deepwater rice internodes, it is induced by gibberellin and wounding, and wound-induced beta-expansin mRNA appears to turn over rapidly.

The method of trend analysis of parameters time series of gas-turbine engine state

NASA Astrophysics Data System (ADS)

Hvozdeva, I.; Myrhorod, V.; Derenh, Y.

2017-10-01

This research substantiates an approach to interval estimation of time series trend component. The well-known methods of spectral and trend analysis are used for multidimensional data arrays. The interval estimation of trend component is proposed for the time series whose autocorrelation matrix possesses a prevailing eigenvalue. The properties of time series autocorrelation matrix are identified.

GeneNetFinder2: Improved Inference of Dynamic Gene Regulatory Relations with Multiple Regulators.

PubMed

Han, Kyungsook; Lee, Jeonghoon

2016-01-01

A gene involved in complex regulatory interactions may have multiple regulators since gene expression in such interactions is often controlled by more than one gene. Another thing that makes gene regulatory interactions complicated is that regulatory interactions are not static, but change over time during the cell cycle. Most research so far has focused on identifying gene regulatory relations between individual genes in a particular stage of the cell cycle. In this study we developed a method for identifying dynamic gene regulations of several types from the time-series gene expression data. The method can find gene regulations with multiple regulators that work in combination or individually as well as those with single regulators. The method has been implemented as the second version of GeneNetFinder (hereafter called GeneNetFinder2) and tested on several gene expression datasets. Experimental results with gene expression data revealed the existence of genes that are not regulated by individual genes but rather by a combination of several genes. Such gene regulatory relations cannot be found by conventional methods. Our method finds such regulatory relations as well as those with multiple, independent regulators or single regulators, and represents gene regulatory relations as a dynamic network in which different gene regulatory relations are shown in different stages of the cell cycle. GeneNetFinder2 is available at http://bclab.inha.ac.kr/GeneNetFinder and will be useful for modeling dynamic gene regulations with multiple regulators.

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Homogenising time series: beliefs, dogmas and facts

NASA Astrophysics Data System (ADS)

Domonkos, P.

2011-06-01

In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

The Timeseries Toolbox - A Web Application to Enable Accessible, Reproducible Time Series Analysis

NASA Astrophysics Data System (ADS)

Veatch, W.; Friedman, D.; Baker, B.; Mueller, C.

2017-12-01

The vast majority of data analyzed by climate researchers are repeated observations of physical process or time series data. This data lends itself of a common set of statistical techniques and models designed to determine trends and variability (e.g., seasonality) of these repeated observations. Often, these same techniques and models can be applied to a wide variety of different time series data. The Timeseries Toolbox is a web application designed to standardize and streamline these common approaches to time series analysis and modeling with particular attention to hydrologic time series used in climate preparedness and resilience planning and design by the U. S. Army Corps of Engineers. The application performs much of the pre-processing of time series data necessary for more complex techniques (e.g. interpolation, aggregation). With this tool, users can upload any dataset that conforms to a standard template and immediately begin applying these techniques to analyze their time series data.

Fuzzy time-series based on Fibonacci sequence for stock price forecasting

NASA Astrophysics Data System (ADS)

Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

2007-07-01

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Time-series modeling of long-term weight self-monitoring data.

PubMed

Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka

2015-08-01

Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

FALSE DETERMINATIONS OF CHAOS IN SHORT NOISY TIME SERIES. (R828745)

EPA Science Inventory

A method (NEMG) proposed in 1992 for diagnosing chaos in noisy time series with 50 or fewer observations entails fitting the time series with an empirical function which predicts an observation in the series from previous observations, and then estimating the rate of divergenc...

New Results in Magnitude and Sign Correlations in Heartbeat Fluctuations for Healthy Persons and Congestive Heart Failure (CHF) Patients

NASA Astrophysics Data System (ADS)

Diosdado, A. Muñoz; Cruz, H. Reyes; Hernández, D. Bueno; Coyt, G. Gálvez; González, J. Arellanes

2008-08-01

Heartbeat fluctuations exhibit temporal structure with fractal and nonlinear features that reflect changes in the neuroautonomic control. In this work we have used the detrended fluctuation analysis (DFA) to analyze heartbeat (RR) intervals of 54 healthy subjects and 40 patients with congestive heart failure during 24 hours; we separate time series for sleep and wake phases. We observe long-range correlations in time series of healthy persons and CHF patients. However, the correlations for CHF patients are weaker than the correlations for healthy persons; this fact has been reported by Ashkenazy et al. [1] but with a smaller group of subjects. In time series of CHF patients there is a crossover, it means that the correlations for high and low frequencies are different, but in time series of healthy persons there are not crossovers even if they are sleeping. These crossovers are more pronounced for CHF patients in the sleep phase. We decompose the heartbeat interval time series into magnitude and sign series, we know that these kinds of signals can exhibit different time organization for the magnitude and sign and the magnitude series relates to nonlinear properties of the original time series, while the sign series relates to the linear properties. Magnitude series are long-range correlated, while the sign series are anticorrelated. Newly, the correlations for healthy persons are different that the correlations for CHF patients both for magnitude and sign time series. In the paper of Ashkenazy et al. they proposed the empirical relation: αsign≈1/2(αoriginal+αmagnitude) for the short-range regime (high frequencies), however, we have found a different relation that in our calculations is valid for short and long-range regime: αsign≈1/4(αoriginal+αmagnitude).

An evaluation of the accuracy of modeled and computed streamflow time-series data for the Ohio River at Hannibal Lock and Dam and at a location upstream from Sardis, Ohio

USGS Publications Warehouse

Koltun, G.F.

2015-01-01

Streamflow hydrographs were plotted for modeled/computed time series for the Ohio River near the USGS Sardis gage and the Ohio River at the Hannibal Lock and Dam. In general, the time series at these two locations compared well. Some notable differences include the exclusive presence of short periods of negative streamflows in the USGS 15-minute time-series data for the gage on the Ohio River above Sardis, Ohio, and the occurrence of several peak streamflows in the USACE gate/hydropower time series for the Hannibal Lock and Dam that were appreciably larger than corresponding peaks in the other time series, including those modeled/computed for the downstream Sardis gage

Using wavelet-feedforward neural networks to improve air pollution forecasting in urban environments.

PubMed

Dunea, Daniel; Pohoata, Alin; Iordache, Stefania

2015-07-01

The paper presents the screening of various feedforward neural networks (FANN) and wavelet-feedforward neural networks (WFANN) applied to time series of ground-level ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM10 and PM2.5 fractions) recorded at four monitoring stations located in various urban areas of Romania, to identify common configurations with optimal generalization performance. Two distinct model runs were performed as follows: data processing using hourly-recorded time series of airborne pollutants during cold months (O3, NO2, and PM10), when residential heating increases the local emissions, and data processing using 24-h daily averaged concentrations (PM2.5) recorded between 2009 and 2012. Dataset variability was assessed using statistical analysis. Time series were passed through various FANNs. Each time series was decomposed in four time-scale components using three-level wavelets, which have been passed also through FANN, and recomposed into a single time series. The agreement between observed and modelled output was evaluated based on the statistical significance (r coefficient and correlation between errors and data). Daubechies db3 wavelet-Rprop FANN (6-4-1) utilization gave positive results for O3 time series optimizing the exclusive use of the FANN for hourly-recorded time series. NO2 was difficult to model due to time series specificity, but wavelet integration improved FANN performances. Daubechies db3 wavelet did not improve the FANN outputs for PM10 time series. Both models (FANN/WFANN) overestimated PM2.5 forecasted values in the last quarter of time series. A potential improvement of the forecasted values could be the integration of a smoothing algorithm to adjust the PM2.5 model outputs.

A novel encoding Lempel-Ziv complexity algorithm for quantifying the irregularity of physiological time series.

PubMed

Zhang, Yatao; Wei, Shoushui; Liu, Hai; Zhao, Lina; Liu, Chengyu

2016-09-01

The Lempel-Ziv (LZ) complexity and its variants have been extensively used to analyze the irregularity of physiological time series. To date, these measures cannot explicitly discern between the irregularity and the chaotic characteristics of physiological time series. Our study compared the performance of an encoding LZ (ELZ) complexity algorithm, a novel variant of the LZ complexity algorithm, with those of the classic LZ (CLZ) and multistate LZ (MLZ) complexity algorithms. Simulation experiments on Gaussian noise, logistic chaotic, and periodic time series showed that only the ELZ algorithm monotonically declined with the reduction in irregularity in time series, whereas the CLZ and MLZ approaches yielded overlapped values for chaotic time series and time series mixed with Gaussian noise, demonstrating the accuracy of the proposed ELZ algorithm in capturing the irregularity, rather than the complexity, of physiological time series. In addition, the effect of sequence length on the ELZ algorithm was more stable compared with those on CLZ and MLZ, especially when the sequence length was longer than 300. A sensitivity analysis for all three LZ algorithms revealed that both the MLZ and the ELZ algorithms could respond to the change in time sequences, whereas the CLZ approach could not. Cardiac interbeat (RR) interval time series from the MIT-BIH database were also evaluated, and the results showed that the ELZ algorithm could accurately measure the inherent irregularity of the RR interval time series, as indicated by lower LZ values yielded from a congestive heart failure group versus those yielded from a normal sinus rhythm group (p < 0.01). Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Extracorporeal circulation as a new experimental pathway for myoblast implantation in mdx mice.

PubMed

Torrente, Y; D'Angelo, M G; Del Bo, R; DeLiso, A; Casati, R; Benti, R; Corti, S; Comi, G P; Gerundini, P; Anichini, A; Scarlato, G; Bresolin, N

1999-01-01

The deficiency of dystrophin, a sarcolemmal associated protein, is responsible for Duchenne muscular dystrophy (DMD). Gene replacement is attractive as a potential therapy. In this article, we describe a new method for myoblast transplantation and expression of dystrophin in skeletal muscle tissue of dystrophin-deficient mdx mouse through iliac vessels extracorporeal circulation. We evaluated the extracorporeal circulation as an alternative route of delivering myoblasts to the target tissue. Two series of experiments were performed with the extracorporeal circulation. In a first series, L6 rat myoblasts, transfected with LacZ reporter gene, were perfused in limbs of 15 rats. In the second series, the muscle limbs of three 6-8-week-old mdx were perfused with myoblasts of donor C57BL10J mice. Before these perfusions, the right tibialis anterior (TA) muscle of the rats and mdx was injected three times at several sites with bupivacaine (BPVC) and hyaluronidase. The ability of injected cells to migrate in the host tissue was assessed in rats by technetium-99m cell labeling. No radioactivity was detected in the lungs, bowels, and liver of animals treated with extracorporeal circulation. The tissue integration and viability of the myoblasts were ultimately confirmed by genetic and histochemical analysis of LacZ reporter gene. Following a single extracorporeal perfusion of myoblasts from donor C57BL10J, sarcolemmal expression of dystrophin was observed in clusters of myofibers in tibialis anterior muscles previously treated with BPVC and hyaluronidase. Furthermore, large clusters of dystrophin-positive fibers were observed in muscles up to 21 days after repeated treatments. These clusters represented an average of 4.2% of the total muscle fibers. These results demonstrate that the extracorporeal circulation allows selective myoblast-mediated gene transfer to muscles, opening new perspectives in muscular dystrophy gene therapy.

Time averaging, ageing and delay analysis of financial time series

NASA Astrophysics Data System (ADS)

Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf

2017-06-01

We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.

Identifying modules of coexpressed transcript units and their organization of Saccharopolyspora erythraea from time series gene expression profiles.

PubMed

Chang, Xiao; Liu, Shuai; Yu, Yong-Tao; Li, Yi-Xue; Li, Yuan-Yuan

2010-08-12

The Saccharopolyspora erythraea genome sequence was released in 2007. In order to look at the gene regulations at whole transcriptome level, an expression microarray was specifically designed on the S. erythraea strain NRRL 2338 genome sequence. Based on these data, we set out to investigate the potential transcriptional regulatory networks and their organization. In view of the hierarchical structure of bacterial transcriptional regulation, we constructed a hierarchical coexpression network at whole transcriptome level. A total of 27 modules were identified from 1255 differentially expressed transcript units (TUs) across time course, which were further classified in to four groups. Functional enrichment analysis indicated the biological significance of our hierarchical network. It was indicated that primary metabolism is activated in the first rapid growth phase (phase A), and secondary metabolism is induced when the growth is slowed down (phase B). Among the 27 modules, two are highly correlated to erythromycin production. One contains all genes in the erythromycin-biosynthetic (ery) gene cluster and the other seems to be associated with erythromycin production by sharing common intermediate metabolites. Non-concomitant correlation between production and expression regulation was observed. Especially, by calculating the partial correlation coefficients and building the network based on Gaussian graphical model, intrinsic associations between modules were found, and the association between those two erythromycin production-correlated modules was included as expected. This work created a hierarchical model clustering transcriptome data into coordinated modules, and modules into groups across the time course, giving insight into the concerted transcriptional regulations especially the regulation corresponding to erythromycin production of S. erythraea. This strategy may be extendable to studies on other prokaryotic microorganisms.

Monitoring Global Food Security with New Remote Sensing Products and Tools

NASA Astrophysics Data System (ADS)

Budde, M. E.; Rowland, J.; Senay, G. B.; Funk, C. C.; Husak, G. J.; Magadzire, T.; Verdin, J. P.

2012-12-01

Global agriculture monitoring is a crucial aspect of monitoring food security in the developing world. The Famine Early Warning Systems Network (FEWS NET) has a long history of using remote sensing and crop modeling to address food security threats in the form of drought, floods, pests, and climate change. In recent years, it has become apparent that FEWS NET requires the ability to apply monitoring and modeling frameworks at a global scale to assess potential impacts of foreign production and markets on food security at regional, national, and local levels. Scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the University of California Santa Barbara (UCSB) Climate Hazards Group have provided new and improved data products as well as visualization and analysis tools in support of the increased mandate for remote monitoring. We present our monitoring products for measuring actual evapotranspiration (ETa), normalized difference vegetation index (NDVI) in a near-real-time mode, and satellite-based rainfall estimates and derivatives. USGS FEWS NET has implemented a Simplified Surface Energy Balance (SSEB) model to produce operational ETa anomalies for Africa and Central Asia. During the growing season, ETa anomalies express surplus or deficit crop water use, which is directly related to crop condition and biomass. We present current operational products and provide supporting validation of the SSEB model. The expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) production system provides FEWS NET with an improved NDVI dataset for crop and rangeland monitoring. eMODIS NDVI provides a reliable data stream with a relatively high spatial resolution (250-m) and short latency period (less than 12 hours) which allows for better operational vegetation monitoring. We provide an overview of these data and cite specific applications for crop monitoring. FEWS NET uses satellite rainfall estimates as inputs for monitoring agricultural food production and driving crop water balance models. We present a series of derived rainfall products and provide an update on efforts to improve satellite-based estimates. We also present advancements in monitoring tools, namely, the Early Warning eXplorer (EWX) and interactive rainfall and NDVI time series viewers. The EWX is a data analysis and visualization tool that allows users to rapidly visualize multiple remote sensing datasets and compare standardized anomaly maps and time series. The interactive time series viewers allow users to analyze rainfall and NDVI time series over multiple spatial domains. New and improved data products and more targeted analysis tools are a necessity as food security monitoring requirements expand and resources become limited.

Modified DTW for a quantitative estimation of the similarity between rainfall time series

NASA Astrophysics Data System (ADS)

Djallel Dilmi, Mohamed; Barthès, Laurent; Mallet, Cécile; Chazottes, Aymeric

2017-04-01

The Precipitations are due to complex meteorological phenomenon and can be described as intermittent process. The spatial and temporal variability of this phenomenon is significant and covers large scales. To analyze and model this variability and / or structure, several studies use a network of rain gauges providing several time series of precipitation measurements. To compare these different time series, the authors compute for each time series some parameters (PDF, rain peak intensity, occurrence, amount, duration, intensity …). However, and despite the calculation of these parameters, the comparison of the parameters between two series of measurements remains qualitative. Due to the advection processes, when different sensors of an observation network measure precipitation time series identical in terms of intermitency or intensities, there is a time lag between the different measured series. Analyzing and extracting relevant information on physical phenomena from these precipitation time series implies the development of automatic analytical methods capable of comparing two time series of precipitation measured by different sensors or at two different locations and thus quantifying the difference / similarity. The limits of the Euclidean distance to measure the similarity between the time series of precipitation have been well demonstrated and explained (eg the Euclidian distance is indeed very sensitive to the effects of phase shift : between two identical but slightly shifted time series, this distance is not negligible). To quantify and analysis these time lag, the correlation functions are well established, normalized and commonly used to measure the spatial dependences that are required by many applications. However, authors generally observed that there is always a considerable scatter of the inter-rain gauge correlation coefficients obtained from the individual pairs of rain gauges. Because of a substantial dispersion of estimated time lag, the interpretation of this inter-correlation is not straightforward. We propose here to use an improvement of the Euclidian distance which integrates the global complexity of the rainfall series. The Dynamic Time Wrapping (DTW) used in speech recognition allows matching two time series instantly different and provide the most probable time lag. However, the original formulation of the DTW suffers from some limitations. In particular, it is not adequate to the rain intermittency. In this study we present an adaptation of the DTW for the analysis of rainfall time series : we used time series from the "Météo France" rain gauge network observed between January 1st, 2007 and December 31st, 2015 on 25 stations located in the Île de France area. Then we analyze the results (eg. The distance, the relationship between the time lag detected by our methods and others measured parameters like speed and direction of the wind…) to show the ability of the proposed similarity to provide usefull information on the rain structure. The possibility of using this measure of similarity to define a quality indicator of a sensor integrated into an observation network is also envisaged.

Daily rainfall forecasting for one year in a single run using Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Unnikrishnan, Poornima; Jothiprakash, V.

2018-06-01

Effective modelling and prediction of smaller time step rainfall is reported to be very difficult owing to its highly erratic nature. Accurate forecast of daily rainfall for longer duration (multi time step) may be exceptionally helpful in the efficient planning and management of water resources systems. Identification of inherent patterns in a rainfall time series is also important for an effective water resources planning and management system. In the present study, Singular Spectrum Analysis (SSA) is utilized to forecast the daily rainfall time series pertaining to Koyna watershed in Maharashtra, India, for 365 days after extracting various components of the rainfall time series such as trend, periodic component, noise and cyclic component. In order to forecast the time series for longer time step (365 days-one window length), the signal and noise components of the time series are forecasted separately and then added together. The results of the study show that the method of SSA could extract the various components of the time series effectively and could also forecast the daily rainfall time series for longer duration such as one year in a single run with reasonable accuracy.

Resummed tree heptagon

NASA Astrophysics Data System (ADS)

Belitsky, A. V.

2018-04-01

The form factor program for the regularized space-time S-matrix in planar maximally supersymmetric gauge theory, known as the pentagon operator product expansion, is formulated in terms of flux-tube excitations propagating on a dual two-dimensional world-sheet, whose dynamics is known exactly as a function of 't Hooft coupling. Both MHV and non-MHV amplitudes are described in a uniform, systematic fashion within this framework, with the difference between the two encoded in coupling-dependent helicity form factors expressed via Zhukowski variables. The nontrivial SU(4) tensor structure of flux-tube transitions is coupling independent and is known for any number of charged excitations from solutions of a system of Watson and Mirror equations. This description allows one to resum the infinite series of form factors and recover the space-time S-matrix exactly in kinematical variables at a given order of perturbation series. Recently, this was done for the hexagon. Presently, we successfully perform resummation for the seven-leg tree NMHV amplitude. To this end, we construct the flux-tube integrands of the fifteen independent Grassmann component of the heptagon with an infinite number of small fermion-antifermion pairs accounted for in NMHV two-channel conformal blocks.

Dealing with gene expression missing data.

PubMed

Brás, L P; Menezes, J C

2006-05-01

Compared evaluation of different methods is presented for estimating missing values in microarray data: weighted K-nearest neighbours imputation (KNNimpute), regression-based methods such as local least squares imputation (LLSimpute) and partial least squares imputation (PLSimpute) and Bayesian principal component analysis (BPCA). The influence in prediction accuracy of some factors, such as methods' parameters, type of data relationships used in the estimation process (i.e. row-wise, column-wise or both), missing rate and pattern and type of experiment [time series (TS), non-time series (NTS) or mixed (MIX) experiments] is elucidated. Improvements based on the iterative use of data (iterative LLS and PLS imputation--ILLSimpute and IPLSimpute), the need to perform initial imputations (modified PLS and Helland PLS imputation--MPLSimpute and HPLSimpute) and the type of relationships employed (KNNarray, LLSarray, HPLSarray and alternating PLS--APLSimpute) are proposed. Overall, it is shown that data set properties (type of experiment, missing rate and pattern) affect the data similarity structure, therefore influencing the methods' performance. LLSimpute and ILLSimpute are preferable in the presence of data with a stronger similarity structure (TS and MIX experiments), whereas PLS-based methods (MPLSimpute, IPLSimpute and APLSimpute) are preferable when estimating NTS missing data.

An allelic series reveals essential roles for FY in plant development in addition to flowering-time control.

PubMed

Henderson, Ian R; Liu, Fuquan; Drea, Sinead; Simpson, Gordon G; Dean, Caroline

2005-08-01

The autonomous pathway functions to promote flowering in Arabidopsis by limiting the accumulation of the floral repressor FLOWERING LOCUS C (FLC). Within this pathway FCA is a plant-specific, nuclear RNA-binding protein, which interacts with FY, a highly conserved eukaryotic polyadenylation factor. FCA and FY function to control polyadenylation site choice during processing of the FCA transcript. Null mutations in the yeast FY homologue Pfs2p are lethal. This raises the question as to whether these essential RNA processing functions are conserved in plants. Characterisation of an allelic series of fy mutations reveals that null alleles are embryo lethal. Furthermore, silencing of FY, but not FCA, is deleterious to growth in Nicotiana. The late-flowering fy alleles are hypomorphic and indicate a requirement for both intact FY WD repeats and the C-terminal domain in repression of FLC. The FY C-terminal domain binds FCA and in vitro assays demonstrate a requirement for both C-terminal FY-PPLPP repeats during this interaction. The expression domain of FY supports its roles in essential and flowering-time functions. Hence, FY may mediate both regulated and constitutive RNA 3'-end processing.

Across light: through colour

NASA Astrophysics Data System (ADS)

Azevedo, Isabel; Richardson, Martin; Bernardo, Luis Miguel

2012-03-01

The speed at which our world is changing is reflected in the shifting way artistic images are created and produced. Holography can be used as a medium to express the perception of space with light and colour and to make the material and the immaterial experiments with optical and digital holography. This paper intends to be a reflection on the final product of that process surrounding a debate of ideas for new experimental methodologies applied to holographic images. Holography is a time-based medium and the irretrievable linear flow of time is responsible for a drama, unique to traditional cinematography. If the viewers move to left or right, they see glimpses of the next scene or the previous one perceived a second ago. This interaction of synthetic space arises questions such as: can we see, in "reality", two forms in the same space? Trying to answer this question, a series of works has been created. These concepts are embryonic to a series of digital art holograms and lenticulars technique's titled "Across Light: Through Colour". They required some technical research and comparison between effects from different camera types, using Canon IS3 and Sony HDR CX105.

Dynamic DNA methylation reconfiguration during seed development and germination.

PubMed

Kawakatsu, Taiji; Nery, Joseph R; Castanon, Rosa; Ecker, Joseph R

2017-09-15

Unlike animals, plants can pause their life cycle as dormant seeds. In both plants and animals, DNA methylation is involved in the regulation of gene expression and genome integrity. In animals, reprogramming erases and re-establishes DNA methylation during development. However, knowledge of reprogramming or reconfiguration in plants has been limited to pollen and the central cell. To better understand epigenetic reconfiguration in the embryo, which forms the plant body, we compared time-series methylomes of dry and germinating seeds to publicly available seed development methylomes. Time-series whole genome bisulfite sequencing reveals extensive gain of CHH methylation during seed development and drastic loss of CHH methylation during germination. These dynamic changes in methylation mainly occur within transposable elements. Active DNA methylation during seed development depends on both RNA-directed DNA methylation and heterochromatin formation pathways, whereas global demethylation during germination occurs in a passive manner. However, an active DNA demethylation pathway is initiated during late seed development. This study provides new insights into dynamic DNA methylation reprogramming events during seed development and germination and suggests possible mechanisms of regulation. The observed sequential methylation/demethylation cycle suggests an important role of DNA methylation in seed dormancy.

Cross-correlation of point series using a new method

NASA Technical Reports Server (NTRS)

Strothers, Richard B.

1994-01-01

Traditional methods of cross-correlation of two time series do not apply to point time series. Here, a new method, devised specifically for point series, utilizes a correlation measure that is based in the rms difference (or, alternatively, the median absolute difference) between nearest neightbors in overlapped segments of the two series. Error estimates for the observed locations of the points, as well as a systematic shift of one series with respect to the other to accommodate a constant, but unknown, lead or lag, are easily incorporated into the analysis using Monte Carlo techniques. A methodological restriction adopted here is that one series be treated as a template series against which the other, called the target series, is cross-correlated. To estimate a significance level for the correlation measure, the adopted alternative (null) hypothesis is that the target series arises from a homogeneous Poisson process. The new method is applied to cross-correlating the times of the greatest geomagnetic storms with the times of maximum in the undecennial solar activity cycle.

Self-organising mixture autoregressive model for non-stationary time series modelling.

PubMed

Ni, He; Yin, Hujun

2008-12-01

Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Analysis of Zenith Tropospheric Delay above Europe based on long time series derived from the EPN data

NASA Astrophysics Data System (ADS)

Baldysz, Zofia; Nykiel, Grzegorz; Figurski, Mariusz; Szafranek, Karolina; Kroszczynski, Krzysztof; Araszkiewicz, Andrzej

2015-04-01

In recent years, the GNSS system began to play an increasingly important role in the research related to the climate monitoring. Based on the GPS system, which has the longest operational capability in comparison with other systems, and a common computational strategy applied to all observations, long and homogeneous ZTD (Zenith Tropospheric Delay) time series were derived. This paper presents results of analysis of 16-year ZTD time series obtained from the EPN (EUREF Permanent Network) reprocessing performed by the Military University of Technology. To maintain the uniformity of data, analyzed period of time (1998-2013) is exactly the same for all stations - observations carried out before 1998 were removed from time series and observations processed using different strategy were recalculated according to the MUT LAC approach. For all 16-year time series (59 stations) Lomb-Scargle periodograms were created to obtain information about the oscillations in ZTD time series. Due to strong annual oscillations which disturb the character of oscillations with smaller amplitude and thus hinder their investigation, Lomb-Scargle periodograms for time series with the deleted annual oscillations were created in order to verify presence of semi-annual, ter-annual and quarto-annual oscillations. Linear trend and seasonal components were estimated using LSE (Least Square Estimation) and Mann-Kendall trend test were used to confirm the presence of linear trend designated by LSE method. In order to verify the effect of the length of time series on the estimated size of the linear trend, comparison between two different length of ZTD time series was performed. To carry out a comparative analysis, 30 stations which have been operating since 1996 were selected. For these stations two periods of time were analyzed: shortened 16-year (1998-2013) and full 18-year (1996-2013). For some stations an additional two years of observations have significant impact on changing the size of linear trend - only for 4 stations the size of linear trend was exactly the same for two periods of time. In one case, the nature of the trend has changed from negative (16-year time series) for positive (18-year time series). The average value of a linear trends for 16-year time series is 1,5 mm/decade, but their spatial distribution is not uniform. The average value of linear trends for all 18-year time series is 2,0 mm/decade, with better spatial distribution and smaller discrepancies.

Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

NASA Astrophysics Data System (ADS)

zhang, L.

2011-12-01

Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be studied through the copula theory. As to the parameter estimation, the maximum likelihood estimation (MLE) will be applied. To illustrate the method, the univariate time series model and the dependence structure will be determined and tested using the monthly discharge time series of Cuyahoga River Basin.

Using First Differences to Reduce Inhomogeneity in Radiosonde Temperature Datasets.

NASA Astrophysics Data System (ADS)

Free, Melissa; Angell, James K.; Durre, Imke; Lanzante, John; Peterson, Thomas C.; Seidel, Dian J.

2004-11-01

The utility of a “first difference” method for producing temporally homogeneous large-scale mean time series is assessed. Starting with monthly averages, the method involves dropping data around the time of suspected discontinuities and then calculating differences in temperature from one year to the next, resulting in a time series of year-to-year differences for each month at each station. These first difference time series are then combined to form large-scale means, and mean temperature time series are constructed from the first difference series. When applied to radiosonde temperature data, the method introduces random errors that decrease with the number of station time series used to create the large-scale time series and increase with the number of temporal gaps in the station time series. Root-mean-square errors for annual means of datasets produced with this method using over 500 stations are estimated at no more than 0.03 K, with errors in trends less than 0.02 K decade-1 for 1960 97 at 500 mb. For a 50-station dataset, errors in trends in annual global means introduced by the first differencing procedure may be as large as 0.06 K decade-1 (for six breaks per series), which is greater than the standard error of the trend. Although the first difference method offers significant resource and labor advantages over methods that attempt to adjust the data, it introduces an error in large-scale mean time series that may be unacceptable in some cases.

Using Disks as Models for Proofs of Series

ERIC Educational Resources Information Center

Somchaipeng, Tongta; Kruatong, Tussatrin; Panijpan, Bhinyo

2012-01-01

Exploring and deriving proofs of closed-form expressions for series can be fun for students. However, for some students, a physical representation of such problems is more meaningful. Various approaches have been designed to help students visualize squares of sums and sums of squares; these approaches may be arithmetic-algebraic or combinatorial…

Parent Express.

ERIC Educational Resources Information Center

Kazanjian, Elise, Ed.

1988-01-01

Intended for use by parents of infants and toddlers, this series of 27 8-page month-by-month newsletters provides research-based information on infant and child development and care from 0 to 36 months. Topics in the series for infants include: becoming a parent; getting ready for child birth; the newborn child; and characteristics of the child at…

Beyond the Hofmeister Series: Ion-Specific Effects on Proteins and Their Biological Functions.

PubMed

Okur, Halil I; Hladílková, Jana; Rembert, Kelvin B; Cho, Younhee; Heyda, Jan; Dzubiella, Joachim; Cremer, Paul S; Jungwirth, Pavel

2017-03-09

Ions differ in their ability to salt out proteins from solution as expressed in the lyotropic or Hofmeister series of cations and anions. Since its first formulation in 1888, this series has been invoked in a plethora of effects, going beyond the original salting out/salting in idea to include enzyme activities and the crystallization of proteins, as well as to processes not involving proteins like ion exchange, the surface tension of electrolytes, or bubble coalescence. Although it has been clear that the Hofmeister series is intimately connected to ion hydration in homogeneous and heterogeneous environments and to ion pairing, its molecular origin has not been fully understood. This situation could have been summarized as follows: Many chemists used the Hofmeister series as a mantra to put a label on ion-specific behavior in various environments, rather than to reach a molecular level understanding and, consequently, an ability to predict a particular effect of a given salt ion on proteins in solutions. In this Feature Article we show that the cationic and anionic Hofmeister series can now be rationalized primarily in terms of specific interactions of salt ions with the backbone and charged side chain groups at the protein surface in solution. At the same time, we demonstrate the limitations of separating Hofmeister effects into independent cationic and anionic contributions due to the electroneutrality condition, as well as specific ion pairing, leading to interactions of ions of opposite polarity. Finally, we outline the route beyond Hofmeister chemistry in the direction of understanding specific roles of ions in various biological functionalities, where generic Hofmeister-type interactions can be complemented or even overruled by particular steric arrangements in various ion binding sites.

Development-related expression patterns of protein-coding and miRNA genes involved in porcine muscle growth.

PubMed

Wang, F J; Jin, L; Guo, Y Q; Liu, R; He, M N; Li, M Z; Li, X W

2014-11-27

Muscle growth and development is associated with remarkable changes in protein-coding and microRNA (miRNA) gene expression. To determine the expression patterns of genes and miRNAs related to muscle growth and development, we measured the expression levels of 25 protein-coding and 16 miRNA genes in skeletal and cardiac muscles throughout 5 developmental stages by quantitative reverse transcription-polymerase chain reaction. The Short Time-Series Expression Miner (STEM) software clustering results showed that growth-related genes were downregulated at all developmental stages in both the psoas major and longissimus dorsi muscles, indicating their involvement in early developmental stages. Furthermore, genes related to muscle atrophy, such as forkhead box 1 and muscle ring finger, showed unregulated expression with increasing age, suggesting a decrease in protein synthesis during the later stages of skeletal muscle development. We found that development of the cardiac muscle was a complex process in which growth-related genes were highly expressed during embryonic development, but they did not show uniform postnatal expression patterns. Moreover, the expression level of miR-499, which enhances the expression of the β-myosin heavy chain, was significantly different in the psoas major and longissimus dorsi muscles, suggesting the involvement of miR-499 in the determination of skeletal muscle fiber types. We also performed correlation analyses of messenger RNA and miRNA expression. We found negative relationships between miR-486 and forkhead box 1, and miR-133a and serum response factor at all developmental stages, suggesting that forkhead box 1 and serum response factor are potential targets of miR-486 and miR-133a, respectively.

Radiocarbon dating uncertainty and the reliability of the PEWMA method of time-series analysis for research on long-term human-environment interaction

PubMed Central

Carleton, W. Christopher; Campbell, David

2018-01-01

Statistical time-series analysis has the potential to improve our understanding of human-environment interaction in deep time. However, radiocarbon dating—the most common chronometric technique in archaeological and palaeoenvironmental research—creates challenges for established statistical methods. The methods assume that observations in a time-series are precisely dated, but this assumption is often violated when calibrated radiocarbon dates are used because they usually have highly irregular uncertainties. As a result, it is unclear whether the methods can be reliably used on radiocarbon-dated time-series. With this in mind, we conducted a large simulation study to investigate the impact of chronological uncertainty on a potentially useful time-series method. The method is a type of regression involving a prediction algorithm called the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed for use with count time-series data, which makes it applicable to a wide range of questions about human-environment interaction in deep time. Our simulations suggest that the PEWMA method can often correctly identify relationships between time-series despite chronological uncertainty. When two time-series are correlated with a coefficient of 0.25, the method is able to identify that relationship correctly 20–30% of the time, providing the time-series contain low noise levels. With correlations of around 0.5, it is capable of correctly identifying correlations despite chronological uncertainty more than 90% of the time. While further testing is desirable, these findings indicate that the method can be used to test hypotheses about long-term human-environment interaction with a reasonable degree of confidence. PMID:29351329

Radiocarbon dating uncertainty and the reliability of the PEWMA method of time-series analysis for research on long-term human-environment interaction.

PubMed

Carleton, W Christopher; Campbell, David; Collard, Mark

2018-01-01

Statistical time-series analysis has the potential to improve our understanding of human-environment interaction in deep time. However, radiocarbon dating-the most common chronometric technique in archaeological and palaeoenvironmental research-creates challenges for established statistical methods. The methods assume that observations in a time-series are precisely dated, but this assumption is often violated when calibrated radiocarbon dates are used because they usually have highly irregular uncertainties. As a result, it is unclear whether the methods can be reliably used on radiocarbon-dated time-series. With this in mind, we conducted a large simulation study to investigate the impact of chronological uncertainty on a potentially useful time-series method. The method is a type of regression involving a prediction algorithm called the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed for use with count time-series data, which makes it applicable to a wide range of questions about human-environment interaction in deep time. Our simulations suggest that the PEWMA method can often correctly identify relationships between time-series despite chronological uncertainty. When two time-series are correlated with a coefficient of 0.25, the method is able to identify that relationship correctly 20-30% of the time, providing the time-series contain low noise levels. With correlations of around 0.5, it is capable of correctly identifying correlations despite chronological uncertainty more than 90% of the time. While further testing is desirable, these findings indicate that the method can be used to test hypotheses about long-term human-environment interaction with a reasonable degree of confidence.

Short Term Rain Prediction For Sustainability of Tanks in the Tropic Influenced by Shadow Rains

NASA Astrophysics Data System (ADS)

Suresh, S.

2007-07-01

Rainfall and flow prediction, adapting the Venkataraman single time series approach and Wiener multiple time series approach were conducted for Aralikottai tank system, and Kothamangalam tank system, Tamilnadu, India. The results indicated that the raw prediction of daily values is closer to actual values than trend identified predictions. The sister seasonal time series were more amenable for prediction than whole parent time series. Venkataraman single time approach was more suited for rainfall prediction. Wiener approach proved better for daily prediction of flow based on rainfall. The major conclusion is that the sister seasonal time series of rain and flow have their own identities even though they form part of the whole parent time series. Further studies with other tropical small watersheds are necessary to establish this unique characteristic of independent but not exclusive behavior of seasonal stationary stochastic processes as compared to parent non stationary stochastic processes.

The Value of Interrupted Time-Series Experiments for Community Intervention Research

PubMed Central

Biglan, Anthony; Ary, Dennis; Wagenaar, Alexander C.

2015-01-01

Greater use of interrupted time-series experiments is advocated for community intervention research. Time-series designs enable the development of knowledge about the effects of community interventions and policies in circumstances in which randomized controlled trials are too expensive, premature, or simply impractical. The multiple baseline time-series design typically involves two or more communities that are repeatedly assessed, with the intervention introduced into one community at a time. It is particularly well suited to initial evaluations of community interventions and the refinement of those interventions. This paper describes the main features of multiple baseline designs and related repeated-measures time-series experiments, discusses the threats to internal validity in multiple baseline designs, and outlines techniques for statistical analyses of time-series data. Examples are given of the use of multiple baseline designs in evaluating community interventions and policy changes. PMID:11507793

Nonlinear theory for laminated and thick plates and shells including the effects of transverse shearing

NASA Technical Reports Server (NTRS)

Stein, M.

1985-01-01

Nonlinear strain displacement relations for three-dimensional elasticity are determined in orthogonal curvilinear coordinates. To develop a two-dimensional theory, the displacements are expressed by trigonometric series representation through-the-thickness. The nonlinear strain-displacement relations are expanded into series which contain all first and second degree terms. In the series for the displacements only the first few terms are retained. Insertion of the expansions into the three-dimensional virtual work expression leads to nonlinear equations of equilibrium for laminated and thick plates and shells that include the effects of transverse shearing. Equations of equilibrium and buckling equations are derived for flat plates and cylindrical shells. The shell equations reduce to conventional transverse shearing shell equations when the effects of the trigonometric terms are omitted and to classical shell equations when the trigonometric terms are omitted and the shell is assumed to be thin.

Rainfall disaggregation for urban hydrology: Effects of spatial consistence

NASA Astrophysics Data System (ADS)

Müller, Hannes; Haberlandt, Uwe

2015-04-01

For urban hydrology rainfall time series with a high temporal resolution are crucial. Observed time series of this kind are very short in most cases, so they cannot be used. On the contrary, time series with lower temporal resolution (daily measurements) exist for much longer periods. The objective is to derive time series with a long duration and a high resolution by disaggregating time series of the non-recording stations with information of time series of the recording stations. The multiplicative random cascade model is a well-known disaggregation model for daily time series. For urban hydrology it is often assumed, that a day consists of only 1280 minutes in total as starting point for the disaggregation process. We introduce a new variant for the cascade model, which is functional without this assumption and also outperforms the existing approach regarding time series characteristics like wet and dry spell duration, average intensity, fraction of dry intervals and extreme value representation. However, in both approaches rainfall time series of different stations are disaggregated without consideration of surrounding stations. This yields in unrealistic spatial patterns of rainfall. We apply a simulated annealing algorithm that has been used successfully for hourly values before. Relative diurnal cycles of the disaggregated time series are resampled to reproduce the spatial dependence of rainfall. To describe spatial dependence we use bivariate characteristics like probability of occurrence, continuity ratio and coefficient of correlation. Investigation area is a sewage system in Northern Germany. We show that the algorithm has the capability to improve spatial dependence. The influence of the chosen disaggregation routine and the spatial dependence on overflow occurrences and volumes of the sewage system will be analyzed.

Clinical time series prediction: Toward a hierarchical dynamical system framework.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

The Legacy of Benoit Mandelbrot in Geophysics

NASA Astrophysics Data System (ADS)

Turcotte, D. L.

2001-12-01

The concept of fractals (fractional dimension) was introduced by Benoit Mandelbrot in his famous 1967 Science paper. The initial application was to the length of the coastline of Britain. A milestone in the appreciation of the fractal concept by geophysicists was the Union session of the AGU on fractals led off by Benoit in 1986. Although fractals have found important applications in almost every branch of the physical, biological, and social sciences, fractals have been particularly useful in geophysics. Drainage networks are fractal. The frequency-magnitude distribution of earthquakes is fractal. The scale invariance of landscapes and many other geological processes is due to the applicability of power-law (fractal) distributions. Clouds are often fractal. Porosity distributions are fractal. In an almost independent line of research, Benoit in collaboration with James Wallace and others developed the concept of self-affine fractals. The original applications were primarily to time series in hydrology and built on the foundation laid by Henry Hurst. Fractional Gaussian noises and fractional Brownian motions are ubiquitous in geophysics. These are expressed in terms of the power-law relation between the power-spectral density S and frequency f, S ~ f{ β }, examples are β = 0 (white noise), β = 1 (1/f noise), β = 2 (Brownian motion). Of particular importance in geophysics are fractional noises with β = 0.5, these are stationary but have long-range persistent and have a Hurst exponent H = 0.7. Examples include river flows, tree rings, sunspots, varves, etc. Two of Benoit Mandelbrot's major contributions in geophysics as in other fields are: (1) an appreciation of the importance of fat-tail, power-law (fractal) distributions and (2) an appreciation of the importance of self-similar long-range persistence in both stationary time series (noises) and nonstationary time series (walks).

Extraction and Analysis of Regional Emission and Absorption Events of Greenhouse Gases with GOSAT and OCO-2

NASA Astrophysics Data System (ADS)

Kasai, K.; Shiomi, K.; Konno, A.; Tadono, T.; Hori, M.

2016-12-01

Global observation of greenhouse gases such as carbon dioxide (CO2) and methane (CH4) with high spatio-temporal resolution and accurate estimation of sources and sinks are important to understand greenhouse gases dynamics. Greenhouse Gases Observing Satellite (GOSAT) has observed column-averaged dry-air mole fractions of CO2 (XCO2) and CH4 (XCH4) over 7 years since January 2009 with wide swath but sparse pointing. Orbiting Carbon Observatory-2 (OCO-2) has observed XCO2 jointly on orbit since July 2014 with narrow swath but high resolution. We use two retrieved datasets as GOSAT observation data. One is ACOS GOSAT/TANSO-FTS Level 2 Full Product by NASA/JPL, and the other is NIES TANSO-FTS L2 column amount (SWIR). By using these GOSAT datasets and OCO-2 L2 Full Product, the biases among datasets, local sources and sinks, and temporal variability of greenhouse gases are clarified. In addition, CarbonTracker, which is a global model of atmospheric CO2 and CH4 developed by NOAA/ESRL, are also analyzed for comparing between satellite observation data and atmospheric model data. Before analyzing these datasets, outliers are screened by using quality flag, outcome flag, and warn level in land or sea parts. Time series data of XCO2 and XCH4 are obtained globally from satellite observation and atmospheric model datasets, and functions which express typical inter-annual and seasonal variation are fitted to each spatial grid. Consequently, anomalous events of XCO2 and XCH4 are extracted by the difference between each time series dataset and the fitted function. Regional emission and absorption events are analyzed by time series variation of satellite observation data and by comparing with atmospheric model data.

MODIS-Aqua detects Noctiluca scintillans and hotspots in the central Arabian Sea.

PubMed

Dwivedi, R; Priyaja, P; Rafeeq, M; Sudhakar, M

2016-01-01

Northern Arabian Sea is considered as an ecologically sensitive area as it experiences a massive upwelling and long-lasting algal bloom, Noctiluca scintillans (green tide) during summer and spring-winter, respectively. Diatom bloom is also found to be co-located with N. scintillans and both have an impact on ecology of the basin. In-house technique of detecting species of these blooms from Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua data was used to generate a time-series of images revealing their spatial distribution. A study of spatial-temporal variability of these blooms using satellite data expressed a cyclic pattern of their spread over a period of 13 years. An average distribution of the blooms for January-March period revealed a peak in 2015 and minimum in 2013. Subsequently, a time-series of phytoplankton species images were generated for these 2 years to study their inter-annual variability and the associated factors. Species images during active phase of the bloom (February) in 2015 indicated development of N. scintillans and diatom in the central Arabian Sea also, up to 12° N. This observation was substantiated with relevant oceanic parameters measured from the ship as well as satellite data and the same is highlight of the paper. While oxygen depletion and release of ammonia associated with N. scintillans are detrimental for waters on the western side; it is relatively less extreme and supports the entire food chain on the eastern side. In view of these contrasting eco-sensitive events, it is a matter of concern to identify biologically active persistent areas, hot spots, in order to study their ecology in detail. An ecological index, persistence of the bloom, was derived from the time-series of species images and it is another highlight of our study.

Proposal of Classification Method of Time Series Data in International Emissions Trading Market Using Agent-based Simulation

NASA Astrophysics Data System (ADS)

Nakada, Tomohiro; Takadama, Keiki; Watanabe, Shigeyoshi

This paper proposes the classification method using Bayesian analytical method to classify the time series data in the international emissions trading market depend on the agent-based simulation and compares the case with Discrete Fourier transform analytical method. The purpose demonstrates the analytical methods mapping time series data such as market price. These analytical methods have revealed the following results: (1) the classification methods indicate the distance of mapping from the time series data, it is easier the understanding and inference than time series data; (2) these methods can analyze the uncertain time series data using the distance via agent-based simulation including stationary process and non-stationary process; and (3) Bayesian analytical method can show the 1% difference description of the emission reduction targets of agent.

How long will the traffic flow time series keep efficacious to forecast the future?

NASA Astrophysics Data System (ADS)

Yuan, PengCheng; Lin, XuXun

2017-02-01

This paper investigate how long will the historical traffic flow time series keep efficacious to forecast the future. In this frame, we collect the traffic flow time series data with different granularity at first. Then, using the modified rescaled range analysis method, we analyze the long memory property of the traffic flow time series by computing the Hurst exponent. We calculate the long-term memory cycle and test its significance. We also compare it with the maximum Lyapunov exponent method result. Our results show that both of the freeway traffic flow time series and the ground way traffic flow time series demonstrate positively correlated trend (have long-term memory property), both of their memory cycle are about 30 h. We think this study is useful for the short-term or long-term traffic flow prediction and management.

Measurements of spatial population synchrony: influence of time series transformations.

PubMed

Chevalier, Mathieu; Laffaille, Pascal; Ferdy, Jean-Baptiste; Grenouillet, Gaël

2015-09-01

Two mechanisms have been proposed to explain spatial population synchrony: dispersal among populations, and the spatial correlation of density-independent factors (the "Moran effect"). To identify which of these two mechanisms is driving spatial population synchrony, time series transformations (TSTs) of abundance data have been used to remove the signature of one mechanism, and highlight the effect of the other. However, several issues with TSTs remain, and to date no consensus has emerged about how population time series should be handled in synchrony studies. Here, by using 3131 time series involving 34 fish species found in French rivers, we computed several metrics commonly used in synchrony studies to determine whether a large-scale climatic factor (temperature) influenced fish population dynamics at the regional scale, and to test the effect of three commonly used TSTs (detrending, prewhitening and a combination of both) on these metrics. We also tested whether the influence of TSTs on time series and population synchrony levels was related to the features of the time series using both empirical and simulated time series. For several species, and regardless of the TST used, we evidenced a Moran effect on freshwater fish populations. However, these results were globally biased downward by TSTs which reduced our ability to detect significant signals. Depending on the species and the features of the time series, we found that TSTs could lead to contradictory results, regardless of the metric considered. Finally, we suggest guidelines on how population time series should be processed in synchrony studies.

Transition Icons for Time-Series Visualization and Exploratory Analysis.

PubMed

Nickerson, Paul V; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd M; Tighe, Patrick J; Rashidi, Parisa

2018-03-01

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

Relating the large-scale structure of time series and visibility networks.

PubMed

Rodríguez, Miguel A

2017-06-01

The structure of time series is usually characterized by means of correlations. A new proposal based on visibility networks has been considered recently. Visibility networks are complex networks mapped from surfaces or time series using visibility properties. The structures of time series and visibility networks are closely related, as shown by means of fractional time series in recent works. In these works, a simple relationship between the Hurst exponent H of fractional time series and the exponent of the distribution of edges γ of the corresponding visibility network, which exhibits a power law, is shown. To check and generalize these results, in this paper we delve into this idea of connected structures by defining both structures more properly. In addition to the exponents used before, H and γ, which take into account local properties, we consider two more exponents that, as we will show, characterize global properties. These are the exponent α for time series, which gives the scaling of the variance with the size as var∼T^{2α}, and the exponent κ of their corresponding network, which gives the scaling of the averaged maximum of the number of edges, 〈k_{M}〉∼N^{κ}. With this representation, a more precise connection between the structures of general time series and their associated visibility network is achieved. Similarities and differences are more clearly established, and new scaling forms of complex networks appear in agreement with their respective classes of time series.

Finite-time singularities in the dynamics of hyperinflation in an economy

NASA Astrophysics Data System (ADS)

Szybisz, Martín A.; Szybisz, Leszek

2009-08-01

The dynamics of hyperinflation episodes is studied by applying a theoretical approach based on collective “adaptive inflation expectations” with a positive nonlinear feedback proposed in the literature. In such a description it is assumed that the growth rate of the logarithmic price, r(t) , changes with a velocity obeying a power law which leads to a finite-time singularity at a critical time tc . By revising that model we found that, indeed, there are two types of singular solutions for the logarithmic price, p(t) . One is given by the already reported form p(t)≈(tc-t)-α (with α>0 ) and the other exhibits a logarithmic divergence, p(t)≈ln[1/(tc-t)] . The singularity is a signature for an economic crash. In the present work we express p(t) explicitly in terms of the parameters introduced throughout the formulation avoiding the use of any combination of them defined in the original paper. This procedure allows to examine simultaneously the time series of r(t) and p(t) performing a linked error analysis of the determined parameters. For the first time this approach is applied for analyzing the very extreme historical hyperinflations occurred in Greece (1941-1944) and Yugoslavia (1991-1994). The case of Greece is compatible with a logarithmic singularity. The study is completed with an analysis of the hyperinflation spiral currently experienced in Zimbabwe. According to our results, an economic crash in this country is predicted for these days. The robustness of the results to changes of the initial time of the series and the differences with a linear feedback are discussed.